Task description
The aim of this task is to develop anomalous sound detection techniques that can train models on new data with noisy normal machine sounds and a few additional samples containing only factory noise or clean normal machine sounds, enabling the model to achieve higher detection performance regardless of environmental noise shifts or other domain shifts.
More detailed task description can be found in the task description page
Teams ranking
Table including only the best performing system per submitting team.
Rank | Submission Information | Evaluation Dataset | Development Dataset | |||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Submission Code |
Technical Report |
Official Rank |
Official Score |
AutoTrash (AUC) |
AutoTrash (pAUC) |
BandSealer (AUC) |
BandSealer (pAUC) |
CoffeeGrinder (AUC) |
CoffeeGrinder (pAUC) |
HomeCamera (AUC) |
HomeCamera (pAUC) |
Polisher (AUC) |
Polisher (pAUC) |
ScrewFeeder (AUC) |
ScrewFeeder (pAUC) |
ToyPet (AUC) |
ToyPet (pAUC) |
ToyRCCar (AUC) |
ToyRCCar (pAUC) |
ToyCar (AUC) |
ToyCar (pAUC) |
ToyTrain (AUC) |
ToyTrain (pAUC) |
Bearing (AUC) |
Bearing (pAUC) |
Fan (AUC) |
Fan (pAUC) |
Gearbox (AUC) |
Gearbox (pAUC) |
Slider (AUC) |
Slider (pAUC) |
Valve (AUC) |
Valve (pAUC) |
|
DCASE2025_baseline_task2_MAHALA | DCASE2025baseline2025 | 58 | 56.50558189601554 ± 0.0012582648036794197 | 62.59 | 54.16 | 45.77 | 49.11 | 52.52 | 51.42 | 57.05 | 51.84 | 60.34 | 53.79 | 75.85 | 70.05 | 58.88 | 56.84 | 55.67 | 54.00 | 62.04 | 49.05 | 48.51 | 48.32 | 61.33 | 61.86 | 58.27 | 50.82 | 62.44 | 55.07 | 62.03 | 53.61 | 58.61 | 52.53 | |
Zhou_XJU_task2_4 | ZhouXJU2025 | 93 | 52.90746388052138 ± 0.0012225858948732692 | 43.95 | 51.11 | 56.27 | 52.32 | 46.38 | 52.16 | 55.68 | 53.74 | 53.42 | 50.58 | 64.00 | 57.11 | 54.77 | 53.42 | 54.61 | 52.00 | 78.38 | 55.53 | 69.87 | 55.16 | 61.44 | 54.53 | 58.09 | 56.32 | 70.46 | 61.63 | 62.74 | 51.68 | 75.63 | 72.53 | |
Cai_NCUT_task2_3 | CaiNCUT2025 | 42 | 57.5585903081211 ± 0.0013141680391751025 | 75.94 | 61.29 | 57.92 | 54.89 | 45.84 | 52.63 | 48.52 | 50.26 | 55.57 | 51.24 | 83.40 | 67.08 | 59.64 | 58.13 | 59.46 | 52.50 | 69.44 | 53.32 | 73.72 | 55.32 | 66.09 | 62.21 | 53.11 | 51.16 | 57.27 | 54.68 | 68.61 | 56.05 | 80.89 | 73.76 | |
Saengthong_SCITOK_task2_2 | SaengthongSCITOK2025 | 2 | 61.569433614253896 ± 0.001484160042616686 | 86.06 | 71.05 | 62.38 | 57.37 | 53.23 | 52.00 | 52.37 | 52.32 | 67.40 | 56.68 | 84.26 | 73.47 | 67.11 | 57.84 | 52.95 | 51.63 | 67.34 | 55.68 | 77.11 | 59.79 | 65.99 | 60.32 | 54.49 | 56.95 | 68.31 | 58.74 | 70.23 | 57.05 | 81.46 | 72.00 | |
Zhang_DKU_task2_4 | ZhangDKU2025 | 63 | 55.76153468345078 ± 0.001174199773801252 | 82.96 | 65.21 | 56.49 | 51.84 | 55.43 | 51.74 | 45.76 | 55.32 | 58.53 | 54.21 | 68.19 | 56.42 | 52.26 | 52.26 | 47.27 | 50.79 | 66.16 | 48.47 | 68.14 | 50.10 | 70.10 | 56.00 | 57.99 | 53.36 | 81.59 | 73.84 | 72.78 | 55.63 | 85.40 | 86.94 | |
WT_IACAS_task2_2 | WTIACAS2025 | 44 | 57.44073696264025 ± 0.0013512115891784388 | 87.27 | 72.74 | 66.85 | 61.37 | 50.12 | 51.63 | 50.36 | 52.89 | 57.79 | 56.63 | 66.48 | 62.84 | 59.16 | 55.00 | 42.32 | 51.21 | 70.94 | 59.84 | 77.96 | 58.84 | 69.98 | 63.11 | 61.70 | 55.42 | 83.20 | 74.26 | 71.78 | 55.74 | 91.24 | 85.53 | |
Zhou_XAUAT_task2_1 | ZhouXAUAT2025 | 24 | 58.1881454965969 ± 0.001303450752882116 | 90.58 | 79.16 | 55.62 | 50.63 | 55.14 | 51.84 | 62.03 | 55.32 | 62.74 | 54.05 | 52.52 | 51.42 | 61.85 | 53.58 | 51.29 | 54.79 | 63.90 | 53.58 | 71.25 | 52.89 | 63.25 | 59.11 | 63.60 | 61.32 | 73.87 | 57.00 | 63.82 | 52.16 | 79.53 | 68.16 | |
Zhong_USTC_task2_4 | ZhongUSTC2025 | 30 | 57.874646573614505 ± 0.0013941667172153518 | 81.32 | 56.10 | 55.96 | 55.21 | 58.76 | 55.68 | 49.43 | 52.05 | 70.54 | 62.16 | 76.01 | 63.53 | 59.49 | 54.74 | 41.59 | 49.32 | 67.49 | 54.24 | 62.69 | 52.67 | 60.26 | 63.58 | 55.37 | 49.68 | 60.16 | 56.24 | 62.44 | 57.98 | 68.88 | 61.13 | |
Vijayyan_SNUC_task2_1 | VijayyanSNUC2025 | 109 | 49.90505686153821 ± 0.0012811394826572587 | 64.39 | 56.74 | 41.16 | 48.32 | 45.70 | 50.53 | 34.37 | 47.37 | 53.20 | 51.32 | 72.65 | 63.47 | 51.10 | 52.89 | 48.03 | 49.89 | 63.24 | 49.05 | 62.76 | 54.32 | 65.14 | 58.32 | 57.58 | 51.58 | 63.38 | 54.74 | 64.14 | 53.05 | 81.16 | 63.16 | |
CHUNG_KUCAU_task2_4 | CHUNGKUCAU2025 | 47 | 57.227161466047036 ± 0.0013105905782118228 | 77.48 | 67.68 | 67.33 | 56.95 | 48.55 | 51.16 | 63.55 | 57.84 | 56.18 | 52.37 | 74.23 | 67.32 | 47.25 | 47.58 | 46.82 | 51.37 | 66.81 | 51.36 | 70.31 | 558.78 | 72.06 | 57.00 | 51.06 | 51.31 | 61.06 | 52.57 | 65.06 | 50.15 | 78.40 | 67.78 | |
Dung_CNTT1PTIT_task2_1 | DungCNTT1PTIT2025 | 112 | 48.71892564275941 ± 0.0011303602632511981 | 38.71 | 54.58 | 50.06 | 48.68 | 54.41 | 51.74 | 51.51 | 51.05 | 51.62 | 50.58 | 43.46 | 49.11 | 43.03 | 48.68 | 54.54 | 52.47 | 48.32 | 52.63 | 50.39 | 50.26 | 52.78 | 53.95 | 52.22 | 50.58 | 50.47 | 63.32 | 46.94 | 51.21 | 46.73 | 49.53 | |
Zhang_NWPU_task2_2 | ZhangNWPU2025 | 67 | 55.42463764307989 ± 0.0013394739153165399 | 65.80 | 56.95 | 64.48 | 55.95 | 51.36 | 50.84 | 55.55 | 55.11 | 62.68 | 58.32 | 62.28 | 57.53 | 62.47 | 58.32 | 36.46 | 47.95 | 66.58 | 49.32 | 74.98 | 53.53 | 73.08 | 63.53 | 62.60 | 59.16 | 73.08 | 60.21 | 76.20 | 54.74 | 82.10 | 67.95 | |
Chao_BUCT_task2_3 | ChaoBUCT2025 | 108 | 50.13154697532129 ± 0.001259520077091061 | 56.74 | 50.26 | 37.40 | 48.58 | 70.57 | 50.95 | 60.64 | 52.11 | 52.78 | 51.79 | 41.29 | 48.63 | 51.41 | 48.47 | 45.98 | 49.63 | 48.48 | 42.68 | 54.16 | 53.70 | 58.36 | 58.13 | 52.54 | 51.92 | 49.20 | 47.76 | 52.73 | 52.69 | 52.47 | 52.64 | |
Li_XJTLU_task2_2 | LiXJTLU2025 | 87 | 53.469426047012206 ± 0.0012893785514650324 | 76.28 | 68.11 | 52.38 | 51.11 | 42.51 | 52.05 | 53.29 | 53.63 | 52.59 | 50.00 | 54.35 | 50.05 | 52.99 | 51.00 | 56.24 | 49.89 | 68.73 | 53.05 | 66.72 | 57.30 | 58.16 | 50.68 | 57.63 | 48.15 | 55.83 | 54.00 | 60.24 | 52.73 | 75.80 | 64.73 | |
Wang_ZJU_task2_4 | WangZJU2025 | 55 | 56.866003688314095 ± 0.001245781793743034 | 62.41 | 54.53 | 47.02 | 50.05 | 53.62 | 51.84 | 55.58 | 52.32 | 59.04 | 55.00 | 72.29 | 67.58 | 59.15 | 57.00 | 59.98 | 54.84 | 55.38 | 60.16 | 43.75 | 50.37 | 56.36 | 52.88 | 58.27 | 60.16 | 62.12 | 54.57 | 61.64 | 51.84 | 54.34 | 64.68 | |
Lin_IASP_task2_4 | LinIASP2025 | 73 | 54.776114357402165 ± 0.0011741700736731956 | 77.88 | 69.58 | 47.76 | 51.16 | 52.12 | 51.42 | 48.18 | 49.74 | 53.30 | 53.26 | 61.41 | 58.74 | 54.74 | 54.68 | 54.49 | 50.47 | 59.73 | 50.67 | 60.99 | 49.68 | 62.39 | 59.73 | 59.72 | 55.78 | 60.18 | 57.47 | 60.20 | 53.15 | 67.18 | 59.38 | |
Lobanov_ITMO_task2_2 | LobanovITMO2025 | 98 | 52.52595001212435 ± 0.001248375343145035 | 66.47 | 62.26 | 58.00 | 49.89 | 43.83 | 51.74 | 57.19 | 52.89 | 52.98 | 50.42 | 52.88 | 49.74 | 47.93 | 51.21 | 49.63 | 49.16 | 42.54 | 2550.00 | 63.52 | 3988.00 | 52.66 | 57.22 | 55.26 | 55.10 | 53.70 | 54.12 | 43.84 | 45.07 | 65.80 | 65.80 | |
Qian_nivic_task2_2 | Qiannivic2025 | 28 | 58.01974104378345 ± 0.0014035588713639333 | 82.51 | 56.10 | 57.63 | 53.47 | 56.22 | 51.58 | 46.67 | 53.32 | 68.13 | 61.26 | 75.28 | 64.47 | 59.28 | 54.53 | 48.30 | 49.11 | 64.23 | 51.53 | 60.88 | 55.30 | 63.58 | 65.32 | 56.81 | 50.22 | 59.73 | 56.21 | 63.31 | 53.87 | 69.04 | 59.39 | |
Wang_MYPS_task2_3 | WangMYPS2025 | 1 | 61.62755928284949 ± 0.0013535025717832298 | 80.61 | 77.05 | 64.22 | 51.63 | 57.94 | 52.16 | 62.45 | 53.79 | 68.76 | 54.05 | 90.24 | 79.16 | 62.64 | 54.05 | 44.35 | 52.84 | 66.22 | 50.74 | 62.10 | 54.63 | 62.41 | 65.68 | 56.98 | 51.60 | 60.18 | 57.75 | 62.34 | 53.08 | 68.66 | 62.28 | |
Emon_HDK_task2_1 | EmonHDK2025 | 121 | 45.15099353175931 ± 0.0012413329586015724 | 30.49 | 52.26 | 35.37 | 48.47 | 70.66 | 52.21 | 46.39 | 52.68 | 43.90 | 49.58 | 53.52 | 48.58 | 37.77 | 48.32 | 46.00 | 51.84 | 72.55 | 65.40 | 90.05 | 83.60 | 94.20 | 85.80 | 67.35 | 67.50 | 86.35 | 50.80 | 92.15 | 70.30 | 85.95 | 61.40 | |
Fu_CUMT_task2_4 | FuCUMT2025 | 26 | 58.13668049404887 ± 0.0014644138554073664 | 82.07 | 56.10 | 55.06 | 54.95 | 60.47 | 57.53 | 48.92 | 49.32 | 69.41 | 62.95 | 75.01 | 63.26 | 55.68 | 55.37 | 46.10 | 50.11 | 65.22 | 54.44 | 62.06 | 53.99 | 61.73 | 63.65 | 58.73 | 54.06 | 62.35 | 53.25 | 61.84 | 54.52 | 68.70 | 61.88 | |
Ding_HFUU_task2_4 | DingHFUU2025 | 59 | 56.37774443771024 ± 0.001224450673236013 | 65.98 | 51.95 | 46.57 | 49.47 | 53.42 | 52.11 | 52.68 | 52.21 | 58.71 | 53.89 | 70.26 | 67.84 | 59.27 | 57.32 | 59.16 | 54.32 | 62.80 | 49.70 | 58.81 | 50.19 | 58.94 | 58.12 | 58.55 | 61.05 | 61.86 | 50.63 | 61.67 | 55.05 | 64.66 | 57.35 | |
Yang_NBU_task2_1 | YangNBU2025 | 3 | 61.20134568401735 ± 0.0013249535881398125 | 84.76 | 75.37 | 66.41 | 57.79 | 50.09 | 49.63 | 62.99 | 53.37 | 75.64 | 61.32 | 94.22 | 80.00 | 56.83 | 53.21 | 42.80 | 49.74 | 64.06 | 51.79 | 61.85 | 55.90 | 63.34 | 63.56 | 58.19 | 49.97 | 59.95 | 53.25 | 63.96 | 56.00 | 68.71 | 58.42 | |
Kret_CU_task2_1 | KretCU2025 | 115 | 47.90299388774425 ± 0.001155154341464739 | 32.60 | 48.42 | 50.35 | 50.32 | 45.49 | 49.26 | 33.00 | 49.63 | 51.97 | 51.21 | 61.04 | 62.79 | 56.35 | 54.74 | 59.08 | 49.79 | 47.78 | 0.00 | 56.94 | 13.60 | 50.76 | 13.20 | 51.64 | 0.80 | 58.10 | 0.00 | 56.12 | 5.20 | 55.00 | 3.60 | |
Zheng_SJTU-AITHU_task2_2 | ZhengSJTU-AITHU2025 | 11 | 59.49972264254595 ± 0.0013491175199334372 | 94.60 | 85.58 | 68.09 | 58.95 | 52.27 | 47.58 | 56.31 | 55.32 | 58.22 | 54.37 | 67.21 | 53.21 | 64.23 | 56.00 | 47.46 | 52.95 | 66.15 | 53.16 | 73.63 | 57.63 | 67.60 | 58.79 | 61.96 | 55.89 | 82.57 | 67.53 | 84.90 | 59.95 | 90.82 | 84.11 | |
Zhao_CUMT_task2_3 | ZhaoCUMT2025 | 61 | 56.05710040362136 ± 0.0013546424429837578 | 59.85 | 51.05 | 48.25 | 50.89 | 51.73 | 50.26 | 51.71 | 53.37 | 62.63 | 56.84 | 80.06 | 68.63 | 51.79 | 54.26 | 59.71 | 54.21 | 60.14 | 48.84 | 48.57 | 48.74 | 61.26 | 63.21 | 55.85 | 50.32 | 61.96 | 54.53 | 61.93 | 52.58 | 54.28 | 51.31 | |
Ozeki_MELCO_task2_1 | OzekiMELCO2025 | 23 | 58.23359688192865 ± 0.0011667802614379898 | 86.35 | 74.79 | 62.04 | 55.79 | 46.36 | 51.05 | 56.50 | 50.63 | 62.40 | 55.63 | 52.30 | 49.42 | 64.14 | 56.89 | 62.21 | 55.11 | 58.16 | 51.52 | 71.56 | 49.68 | 56.63 | 53.10 | 54.53 | 53.73 | 61.90 | 56.94 | 73.65 | 59.52 | 82.78 | 73.26 | |
Huang_XJU_task2_1 | HuangXJU2025 | 25 | 58.14052430128241 ± 0.0012193313166606692 | 74.01 | 53.63 | 57.15 | 59.37 | 46.98 | 49.21 | 65.68 | 52.79 | 52.08 | 50.42 | 82.14 | 69.32 | 61.78 | 57.68 | 54.54 | 51.00 | 66.22 | 53.63 | 72.08 | 56.16 | 75.86 | 64.95 | 59.20 | 50.58 | 72.02 | 58.32 | 65.55 | 54.21 | 76.20 | 74.74 | |
Fujimura_NU_task2_1 | FujimuraNU2025 | 7 | 59.99466314801132 ± 0.001325555756111588 | 78.10 | 65.53 | 74.27 | 61.84 | 50.36 | 52.00 | 64.66 | 53.68 | 61.79 | 58.47 | 83.84 | 68.89 | 57.33 | 57.05 | 41.64 | 51.89 | 67.08 | 48.63 | 72.48 | 56.79 | 77.74 | 57.21 | 53.60 | 52.05 | 72.86 | 60.58 | 66.99 | 54.47 | 92.52 | 82.58 | |
Jiang_THUEE_task2_2 | JiangTHUEE2025 | 10 | 59.79329753333621 ± 0.0013522826832875893 | 94.98 | 86.21 | 67.95 | 58.26 | 52.66 | 47.37 | 54.77 | 55.89 | 59.64 | 54.68 | 70.12 | 56.89 | 64.66 | 56.37 | 46.94 | 51.47 | 66.90 | 53.00 | 72.71 | 55.95 | 68.93 | 60.58 | 61.91 | 55.00 | 81.95 | 68.32 | 85.56 | 60.26 | 92.04 | 86.37 | |
Bian_TGU_task2_3 | BianTGU2025 | 104 | 51.417480569726926 ± 0.0011698485309061142 | 44.25 | 49.79 | 55.13 | 50.89 | 49.26 | 51.05 | 57.35 | 51.63 | 46.36 | 48.16 | 57.37 | 51.84 | 52.45 | 51.68 | 55.26 | 51.53 | 48.09 | 48.52 | 46.53 | 49.63 | 49.90 | 49.16 | 52.63 | 50.94 | 52.80 | 49.53 | 55.24 | 51.16 | 48.80 | 51.74 | |
Sera_TMU_task2_1 | SeraTMU2025 | 57 | 56.567561042877735 ± 0.0013602097930091965 | 57.68 | 52.00 | 52.72 | 54.05 | 60.92 | 53.21 | 51.90 | 50.58 | 49.11 | 49.47 | 83.39 | 67.21 | 59.66 | 55.74 | 60.14 | 51.32 | 71.28 | 54.63 | 73.08 | 57.42 | 67.80 | 61.36 | 56.06 | 51.79 | 71.52 | 62.42 | 67.38 | 49.42 | 69.48 | 56.63 | |
Kim_DAU_task2_2 | KimDAU2025 | 111 | 48.96578012474582 ± 0.0011669517429796642 | 59.12 | 49.16 | 44.24 | 50.84 | 40.06 | 49.89 | 57.76 | 56.05 | 47.59 | 50.11 | 49.97 | 51.95 | 45.08 | 48.11 | 47.18 | 50.37 | 77.58 | 66.87 | 77.83 | 68.42 | 78.50 | 69.53 | 77.43 | 65.33 | 78.30 | 69.64 | 77.21 | 64.10 | 78.81 | 67.27 | |
Wang_UniS_task2_1 | WangUniS2025 | 34 | 57.754303274825524 ± 0.0012838059463938772 | 86.59 | 72.84 | 58.69 | 55.00 | 50.61 | 51.26 | 60.86 | 54.11 | 54.32 | 53.05 | 58.10 | 52.84 | 63.87 | 57.63 | 50.19 | 53.84 | 60.74 | 49.95 | 66.68 | 50.53 | 52.89 | 50.11 | 56.02 | 53.11 | 71.89 | 72.65 | 78.14 | 56.63 | 86.38 | 78.32 | |
Guan_HEU_task2_1 | GuanHEU2025 | 22 | 58.25344060002535 ± 0.0011865451404030904 | 70.81 | 59.53 | 55.51 | 53.63 | 46.55 | 49.58 | 49.38 | 50.74 | 61.83 | 56.11 | 89.98 | 77.26 | 62.19 | 61.79 | 55.60 | 51.89 | 72.19 | 54.65 | 75.44 | 57.42 | 64.71 | 61.51 | 56.81 | 53.06 | 70.24 | 57.79 | 67.58 | 53.80 | 73.35 | 65.50 | |
Kim_AISTAT_task2_4 | KimAISTAT2025 | 29 | 57.95495072758183 ± 0.0013606493522197662 | 90.21 | 77.47 | 55.11 | 54.53 | 50.96 | 55.32 | 69.84 | 61.53 | 51.39 | 51.63 | 59.97 | 51.58 | 63.67 | 55.79 | 47.60 | 50.47 | 65.72 | 50.42 | 75.68 | 61.68 | 66.04 | 55.58 | 62.08 | 51.84 | 74.80 | 66.89 | 82.20 | 63.79 | 78.12 | 70.42 |
Supplementary metrics (recall, precision, and F1 score)
Rank | Submission Information | Evaluation Dataset | |||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Submission Code |
Technical Report |
Official Rank |
AutoTrash (F1 score) |
AutoTrash (Recall) |
AutoTrash (Precision) |
BandSealer (F1 score) |
BandSealer (Recall) |
BandSealer (Precision) |
CoffeeGrinder (F1 score) |
CoffeeGrinder (Recall) |
CoffeeGrinder (Precision) |
HomeCamera (F1 score) |
HomeCamera (Recall) |
HomeCamera (Precision) |
Polisher (F1 score) |
Polisher (Recall) |
Polisher (Precision) |
ScrewFeeder (F1 score) |
ScrewFeeder (Recall) |
ScrewFeeder (Precision) |
ToyPet (F1 score) |
ToyPet (Recall) |
ToyPet (Precision) |
ToyRCCar (F1 score) |
ToyRCCar (Recall) |
ToyRCCar (Precision) |
|
DCASE2025_baseline_task2_MAHALA | DCASE2025baseline2025 | 58 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Zhou_XJU_task2_4 | ZhouXJU2025 | 93 | 28.65 | 18.15 | 68.06 | 54.38 | 51.43 | 57.69 | 34.63 | 24.37 | 59.82 | 58.48 | 54.10 | 63.64 | 51.58 | 49.81 | 53.48 | 58.70 | 58.85 | 58.55 | 49.70 | 43.93 | 57.21 | 54.00 | 54.00 | 54.00 | |
Cai_NCUT_task2_3 | CaiNCUT2025 | 42 | 68.43 | 63.55 | 74.11 | 50.77 | 47.08 | 55.09 | 35.00 | 24.44 | 61.65 | 55.29 | 54.84 | 55.75 | 53.30 | 48.07 | 59.80 | 75.28 | 73.42 | 77.24 | 52.10 | 42.93 | 66.26 | 54.58 | 54.84 | 54.32 | |
Saengthong_SCITOK_task2_2 | SaengthongSCITOK2025 | 2 | 80.32 | 76.68 | 84.32 | 42.25 | 29.76 | 72.81 | 47.48 | 38.18 | 62.78 | 28.11 | 19.09 | 53.30 | 56.48 | 46.47 | 71.98 | 69.92 | 97.96 | 54.36 | 64.37 | 74.08 | 56.91 | 42.21 | 35.10 | 52.94 | |
Zhang_DKU_task2_4 | ZhangDKU2025 | 63 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
WT_IACAS_task2_2 | WTIACAS2025 | 44 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Zhou_XAUAT_task2_1 | ZhouXAUAT2025 | 24 | 82.05 | 84.71 | 79.56 | 54.81 | 55.36 | 54.27 | 55.75 | 62.60 | 50.24 | 57.69 | 60.00 | 55.56 | 65.82 | 78.00 | 56.93 | 63.10 | 75.95 | 53.96 | 62.51 | 73.78 | 54.22 | 56.57 | 68.44 | 48.20 | |
Zhong_USTC_task2_4 | ZhongUSTC2025 | 30 | 51.94 | 51.92 | 51.96 | 55.01 | 54.98 | 55.04 | 46.96 | 46.98 | 46.94 | 52.78 | 52.53 | 53.03 | 56.97 | 56.98 | 56.96 | 54.90 | 54.84 | 54.96 | 53.91 | 53.93 | 53.89 | 51.49 | 51.69 | 51.30 | |
Vijayyan_SNUC_task2_1 | VijayyanSNUC2025 | 109 | 70.88 | 82.02 | 62.40 | 52.53 | 48.41 | 57.42 | 36.52 | 31.84 | 42.81 | 49.20 | 49.65 | 48.75 | 50.07 | 45.77 | 55.27 | 69.36 | 94.99 | 54.62 | 62.46 | 70.13 | 56.30 | 33.33 | 25.55 | 47.94 | |
CHUNG_KUCAU_task2_4 | CHUNGKUCAU2025 | 47 | 48.92 | 33.92 | 87.72 | 42.98 | 30.71 | 71.58 | 48.73 | 38.40 | 66.67 | 58.06 | 48.00 | 73.47 | 38.43 | 27.00 | 66.67 | 67.86 | 72.65 | 63.67 | 57.12 | 58.23 | 56.04 | 25.44 | 17.78 | 44.69 | |
Dung_CNTT1PTIT_task2_1 | DungCNTT1PTIT2025 | 112 | 64.40 | 94.99 | 48.72 | 46.78 | 42.79 | 51.60 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Zhang_NWPU_task2_2 | ZhangNWPU2025 | 67 | 58.76 | 49.35 | 72.58 | 46.85 | 45.91 | 47.83 | 53.06 | 52.00 | 54.17 | 49.94 | 48.82 | 51.11 | 52.87 | 51.69 | 54.11 | 51.42 | 50.04 | 52.88 | 52.01 | 50.98 | 53.08 | 51.02 | 50.00 | 52.08 | |
Chao_BUCT_task2_3 | ChaoBUCT2025 | 108 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Li_XJTLU_task2_2 | LiXJTLU2025 | 87 | 69.14 | 68.64 | 69.65 | 55.00 | 54.98 | 55.02 | 32.62 | 24.00 | 50.91 | 46.06 | 44.88 | 47.30 | 51.84 | 48.44 | 55.76 | 49.85 | 49.41 | 50.30 | 46.97 | 41.21 | 54.60 | 53.73 | 51.43 | 56.25 | |
Wang_ZJU_task2_4 | WangZJU2025 | 55 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Lin_IASP_task2_4 | LinIASP2025 | 73 | 64.35 | 64.48 | 64.23 | 52.48 | 51.31 | 53.70 | 54.98 | 54.11 | 55.88 | 47.37 | 47.67 | 47.08 | 51.67 | 50.72 | 52.66 | 56.96 | 56.84 | 57.08 | 50.85 | 50.08 | 51.65 | 54.32 | 54.11 | 54.53 | |
Lobanov_ITMO_task2_2 | LobanovITMO2025 | 98 | 68.53 | 98.99 | 52.41 | 44.89 | 37.89 | 55.05 | 32.17 | 25.71 | 42.96 | 23.17 | 14.12 | 64.52 | 34.63 | 27.87 | 45.73 | 28.07 | 19.20 | 52.17 | 32.60 | 25.87 | 44.04 | 43.62 | 38.57 | 50.19 | |
Qian_nivic_task2_2 | Qiannivic2025 | 28 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Wang_MYPS_task2_3 | WangMYPS2025 | 1 | 73.43 | 71.89 | 75.04 | 59.46 | 59.40 | 59.52 | 52.00 | 51.47 | 52.54 | 59.97 | 59.93 | 60.01 | 61.98 | 61.74 | 62.22 | 83.02 | 82.99 | 83.05 | 59.02 | 58.33 | 59.73 | 44.00 | 43.64 | 44.36 | |
Emon_HDK_task2_1 | EmonHDK2025 | 121 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 6.81 | 3.69 | 43.64 | 10.70 | 6.40 | 32.65 | 13.95 | 8.40 | 41.18 | 0.00 | 0.00 | 0.00 | 11.91 | 6.77 | 49.44 | |
Fu_CUMT_task2_4 | FuCUMT2025 | 26 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Ding_HFUU_task2_4 | DingHFUU2025 | 59 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Yang_NBU_task2_1 | YangNBU2025 | 3 | 51.92 | 51.92 | 51.92 | 47.61 | 47.25 | 47.97 | 51.85 | 51.69 | 52.01 | 44.05 | 43.91 | 44.19 | 53.01 | 52.98 | 53.04 | 49.92 | 49.92 | 49.92 | 42.00 | 42.00 | 42.00 | 52.89 | 52.83 | 52.95 | |
Kret_CU_task2_1 | KretCU2025 | 115 | 0.00 | 0.00 | 0.00 | 6.79 | 3.69 | 42.11 | 41.83 | 36.98 | 48.15 | 57.10 | 61.11 | 53.58 | 24.62 | 15.65 | 57.69 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Zheng_SJTU-AITHU_task2_2 | ZhengSJTU-AITHU2025 | 11 | 85.81 | 85.26 | 86.38 | 64.07 | 63.75 | 64.39 | 48.81 | 47.69 | 49.98 | 56.05 | 55.93 | 56.17 | 57.37 | 56.90 | 57.85 | 61.96 | 61.94 | 61.98 | 56.81 | 55.52 | 58.16 | 45.88 | 42.67 | 49.61 | |
Zhao_CUMT_task2_3 | ZhaoCUMT2025 | 61 | 61.50 | 60.85 | 62.16 | 20.92 | 13.22 | 50.17 | 17.33 | 11.16 | 38.71 | 22.16 | 13.33 | 65.57 | 32.73 | 24.00 | 51.43 | 0.00 | 0.00 | 0.00 | 42.91 | 41.48 | 44.44 | 0.00 | 0.00 | 0.00 | |
Ozeki_MELCO_task2_1 | OzekiMELCO2025 | 23 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Huang_XJU_task2_1 | HuangXJU2025 | 25 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Fujimura_NU_task2_1 | FujimuraNU2025 | 7 | 66.89 | 100.00 | 50.25 | 68.19 | 75.95 | 61.86 | 0.00 | 0.00 | 0.00 | 48.60 | 38.77 | 65.12 | 5.19 | 2.67 | 100.00 | 66.67 | 100.00 | 50.00 | 62.57 | 70.74 | 56.09 | 66.67 | 100.00 | 50.00 | |
Jiang_THUEE_task2_2 | JiangTHUEE2025 | 10 | 88.81 | 88.09 | 89.54 | 64.15 | 63.75 | 64.56 | 50.81 | 49.81 | 51.85 | 55.02 | 54.98 | 55.06 | 57.53 | 56.90 | 58.18 | 65.72 | 65.76 | 65.68 | 58.14 | 56.95 | 59.38 | 46.35 | 43.92 | 49.07 | |
Bian_TGU_task2_3 | BianTGU2025 | 104 | 16.27 | 9.60 | 53.33 | 24.95 | 15.75 | 60.00 | 14.76 | 8.40 | 60.87 | 31.33 | 22.61 | 50.98 | 0.00 | 0.00 | 0.00 | 25.51 | 15.75 | 67.02 | 20.33 | 12.31 | 58.39 | 0.00 | 0.00 | 0.00 | |
Sera_TMU_task2_1 | SeraTMU2025 | 57 | 0.00 | 0.00 | 0.00 | 7.17 | 3.75 | 81.08 | 0.00 | 0.00 | 0.00 | 20.00 | 12.00 | 60.00 | 7.01 | 3.69 | 68.57 | 32.79 | 20.00 | 90.91 | 0.00 | 0.00 | 0.00 | 18.43 | 10.91 | 59.41 | |
Kim_DAU_task2_2 | KimDAU2025 | 111 | 48.79 | 45.96 | 52.00 | 16.34 | 9.75 | 50.32 | 54.26 | 62.40 | 48.00 | 58.03 | 73.42 | 47.97 | 55.43 | 60.98 | 50.81 | 19.29 | 12.92 | 38.01 | 28.39 | 19.80 | 50.13 | 25.63 | 18.20 | 43.33 | |
Wang_UniS_task2_1 | WangUniS2025 | 34 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Guan_HEU_task2_1 | GuanHEU2025 | 22 | 60.06 | 54.19 | 67.36 | 29.70 | 19.35 | 63.83 | 40.13 | 30.41 | 58.96 | 27.05 | 18.67 | 49.12 | 33.04 | 22.40 | 62.92 | 63.91 | 47.35 | 98.31 | 40.52 | 28.10 | 72.64 | 32.35 | 21.82 | 62.50 | |
Kim_AISTAT_task2_4 | KimAISTAT2025 | 29 | 79.88 | 85.95 | 74.61 | 37.03 | 26.55 | 61.18 | 23.80 | 14.75 | 61.64 | 66.81 | 62.60 | 71.63 | 37.38 | 30.00 | 49.59 | 66.89 | 100.00 | 50.25 | 68.24 | 97.96 | 52.36 | 53.17 | 58.58 | 48.68 |
Systems ranking
Rank | Submission Information | Evaluation Dataset | Development Dataset | |||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Submission Code |
Technical Report |
Official Rank |
Official Score |
AutoTrash (AUC) |
AutoTrash (pAUC) |
BandSealer (AUC) |
BandSealer (pAUC) |
CoffeeGrinder (AUC) |
CoffeeGrinder (pAUC) |
HomeCamera (AUC) |
HomeCamera (pAUC) |
Polisher (AUC) |
Polisher (pAUC) |
ScrewFeeder (AUC) |
ScrewFeeder (pAUC) |
ToyPet (AUC) |
ToyPet (pAUC) |
ToyRCCar (AUC) |
ToyRCCar (pAUC) |
ToyCar (AUC) |
ToyCar (pAUC) |
ToyTrain (AUC) |
ToyTrain (pAUC) |
Bearing (AUC) |
Bearing (pAUC) |
Fan (AUC) |
Fan (pAUC) |
Gearbox (AUC) |
Gearbox (pAUC) |
Slider (AUC) |
Slider (pAUC) |
Valve (AUC) |
Valve (pAUC) |
|
DCASE2025_baseline_task2_MAHALA | DCASE2025baseline2025 | 58 | 56.50558189601554 ± 0.0012582648036794197 | 62.59 | 54.16 | 45.77 | 49.11 | 52.52 | 51.42 | 57.05 | 51.84 | 60.34 | 53.79 | 75.85 | 70.05 | 58.88 | 56.84 | 55.67 | 54.00 | 62.04 | 49.05 | 48.51 | 48.32 | 61.33 | 61.86 | 58.27 | 50.82 | 62.44 | 55.07 | 62.03 | 53.61 | 58.61 | 52.53 | |
DCASE2025_baseline_task2_MSE | DCASE2025baseline2025 | 77 | 54.42827820713174 ± 0.001271407576916618 | 48.26 | 54.21 | 51.06 | 52.05 | 55.20 | 53.42 | 61.76 | 52.84 | 53.29 | 52.32 | 65.39 | 62.11 | 47.60 | 55.00 | 57.36 | 55.53 | 62.28 | 49.70 | 59.11 | 50.19 | 59.84 | 61.12 | 54.85 | 49.46 | 57.64 | 52.49 | 59.44 | 52.32 | 65.36 | 57.35 | |
Zhou_XJU_task2_1 | ZhouXJU2025 | 100 | 52.416950012493416 ± 0.0011881967859310029 | 46.52 | 49.53 | 54.53 | 50.89 | 43.02 | 50.16 | 48.82 | 52.84 | 54.45 | 51.42 | 66.81 | 59.11 | 56.14 | 54.32 | 55.23 | 53.32 | 74.45 | 56.42 | 72.63 | 56.26 | 61.27 | 52.63 | 56.10 | 54.68 | 68.90 | 60.63 | 62.53 | 52.16 | 76.45 | 74.00 | |
Zhou_XJU_task2_2 | ZhouXJU2025 | 94 | 52.84774417237518 ± 0.0012060424784515957 | 45.39 | 49.53 | 57.64 | 51.89 | 47.06 | 50.68 | 48.90 | 52.95 | 53.89 | 51.47 | 64.51 | 57.37 | 56.26 | 53.79 | 55.46 | 53.42 | 75.06 | 54.37 | 71.45 | 55.47 | 61.36 | 53.42 | 57.61 | 56.84 | 69.69 | 60.42 | 62.05 | 52.11 | 75.54 | 72.53 | |
Zhou_XJU_task2_3 | ZhouXJU2025 | 96 | 52.63120572932777 ± 0.001226939985877493 | 45.07 | 51.05 | 54.38 | 50.47 | 43.47 | 51.26 | 52.96 | 54.11 | 53.83 | 51.05 | 64.26 | 59.74 | 55.00 | 53.58 | 56.44 | 53.74 | 76.41 | 57.84 | 70.77 | 55.21 | 61.64 | 53.37 | 57.28 | 55.58 | 70.54 | 60.79 | 62.28 | 52.47 | 77.47 | 73.37 | |
Zhou_XJU_task2_4 | ZhouXJU2025 | 93 | 52.90746388052138 ± 0.0012225858948732692 | 43.95 | 51.11 | 56.27 | 52.32 | 46.38 | 52.16 | 55.68 | 53.74 | 53.42 | 50.58 | 64.00 | 57.11 | 54.77 | 53.42 | 54.61 | 52.00 | 78.38 | 55.53 | 69.87 | 55.16 | 61.44 | 54.53 | 58.09 | 56.32 | 70.46 | 61.63 | 62.74 | 51.68 | 75.63 | 72.53 | |
Cai_NCUT_task2_1 | CaiNCUT2025 | 45 | 57.34434703825625 ± 0.0011704510303176085 | 85.12 | 66.26 | 55.49 | 56.26 | 44.55 | 52.47 | 50.09 | 50.05 | 56.70 | 50.21 | 79.78 | 63.84 | 59.23 | 58.21 | 54.73 | 53.05 | 71.57 | 55.63 | 73.17 | 55.11 | 68.21 | 61.05 | 52.77 | 50.37 | 56.90 | 53.53 | 69.17 | 54.16 | 81.53 | 74.84 | |
Cai_NCUT_task2_2 | CaiNCUT2025 | 83 | 54.05784411973193 ± 0.0012580742078938656 | 50.26 | 53.68 | 53.30 | 52.63 | 54.78 | 52.79 | 54.91 | 53.42 | 54.09 | 51.11 | 63.56 | 58.42 | 46.96 | 54.74 | 57.66 | 56.32 | 62.46 | 49.74 | 57.57 | 51.11 | 57.47 | 59.89 | 54.54 | 49.53 | 56.03 | 52.16 | 58.37 | 51.32 | 64.46 | 58.68 | |
Cai_NCUT_task2_3 | CaiNCUT2025 | 42 | 57.5585903081211 ± 0.0013141680391751025 | 75.94 | 61.29 | 57.92 | 54.89 | 45.84 | 52.63 | 48.52 | 50.26 | 55.57 | 51.24 | 83.40 | 67.08 | 59.64 | 58.13 | 59.46 | 52.50 | 69.44 | 53.32 | 73.72 | 55.32 | 66.09 | 62.21 | 53.11 | 51.16 | 57.27 | 54.68 | 68.61 | 56.05 | 80.89 | 73.76 | |
Saengthong_SCITOK_task2_1 | SaengthongSCITOK2025 | 84 | 53.81478512150386 ± 0.0013067715610662351 | 56.90 | 54.68 | 51.40 | 54.21 | 45.53 | 50.89 | 38.31 | 50.26 | 62.41 | 53.00 | 78.80 | 67.32 | 57.30 | 58.00 | 53.69 | 52.74 | 69.31 | 54.37 | 78.20 | 60.53 | 65.70 | 60.21 | 59.86 | 51.63 | 64.84 | 56.21 | 69.80 | 56.58 | 81.99 | 73.58 | |
Saengthong_SCITOK_task2_2 | SaengthongSCITOK2025 | 2 | 61.569433614253896 ± 0.001484160042616686 | 86.06 | 71.05 | 62.38 | 57.37 | 53.23 | 52.00 | 52.37 | 52.32 | 67.40 | 56.68 | 84.26 | 73.47 | 67.11 | 57.84 | 52.95 | 51.63 | 67.34 | 55.68 | 77.11 | 59.79 | 65.99 | 60.32 | 54.49 | 56.95 | 68.31 | 58.74 | 70.23 | 57.05 | 81.46 | 72.00 | |
Saengthong_SCITOK_task2_3 | SaengthongSCITOK2025 | 56 | 56.63642648298707 ± 0.0011745609201499784 | 73.11 | 58.11 | 58.62 | 55.74 | 45.14 | 50.42 | 48.13 | 50.21 | 42.69 | 49.16 | 91.26 | 81.63 | 66.58 | 57.68 | 59.03 | 52.89 | 67.85 | 52.21 | 74.38 | 54.53 | 61.53 | 60.84 | 53.07 | 50.95 | 60.11 | 52.84 | 67.82 | 55.47 | 77.11 | 71.47 | |
Saengthong_SCITOK_task2_4 | SaengthongSCITOK2025 | 80 | 54.32596489450181 ± 0.0013284693479479592 | 51.83 | 50.89 | 54.48 | 52.79 | 45.82 | 50.42 | 45.48 | 50.05 | 65.83 | 56.00 | 72.32 | 61.89 | 57.44 | 59.16 | 53.23 | 52.32 | 66.99 | 50.53 | 77.32 | 55.21 | 59.30 | 56.74 | 58.43 | 49.00 | 62.73 | 57.63 | 69.04 | 59.89 | 76.48 | 67.58 | |
Zhang_DKU_task2_1 | ZhangDKU2025 | 88 | 53.4615898288795 ± 0.001179781217054352 | 68.01 | 60.11 | 51.87 | 54.58 | 58.40 | 51.84 | 41.09 | 52.05 | 53.32 | 51.05 | 64.13 | 54.05 | 53.12 | 52.11 | 48.46 | 50.84 | 67.80 | 52.68 | 71.21 | 52.10 | 65.24 | 58.10 | 54.92 | 53.05 | 80.70 | 69.36 | 66.31 | 53.47 | 85.73 | 85.63 | |
Zhang_DKU_task2_2 | ZhangDKU2025 | 70 | 55.18393915462276 ± 0.0012646610416560881 | 83.81 | 67.21 | 48.62 | 51.00 | 56.66 | 50.58 | 46.64 | 52.00 | 56.82 | 53.58 | 59.96 | 54.00 | 54.57 | 53.11 | 53.89 | 50.21 | 69.27 | 49.78 | 69.63 | 49.31 | 71.60 | 61.31 | 59.42 | 55.26 | 81.64 | 70.00 | 66.06 | 52.73 | 84.77 | 85.89 | |
Zhang_DKU_task2_3 | ZhangDKU2025 | 92 | 52.9797667433996 ± 0.0011534403766833834 | 75.94 | 57.21 | 55.94 | 52.11 | 41.86 | 48.84 | 51.58 | 54.63 | 59.23 | 56.53 | 61.80 | 56.47 | 52.19 | 50.05 | 40.91 | 49.53 | 68.69 | 49.68 | 71.00 | 50.42 | 71.08 | 56.21 | 57.75 | 53.21 | 84.36 | 76.15 | 72.37 | 55.57 | 86.47 | 87.73 | |
Zhang_DKU_task2_4 | ZhangDKU2025 | 63 | 55.76153468345078 ± 0.001174199773801252 | 82.96 | 65.21 | 56.49 | 51.84 | 55.43 | 51.74 | 45.76 | 55.32 | 58.53 | 54.21 | 68.19 | 56.42 | 52.26 | 52.26 | 47.27 | 50.79 | 66.16 | 48.47 | 68.14 | 50.10 | 70.10 | 56.00 | 57.99 | 53.36 | 81.59 | 73.84 | 72.78 | 55.63 | 85.40 | 86.94 | |
WT_IACAS_task2_1 | WTIACAS2025 | 66 | 55.53360456952694 ± 0.0012887031567615573 | 79.64 | 67.26 | 64.70 | 58.58 | 51.79 | 53.11 | 50.89 | 52.95 | 56.33 | 57.16 | 59.06 | 56.53 | 55.40 | 53.95 | 40.24 | 50.84 | 67.16 | 54.95 | 78.26 | 58.42 | 68.78 | 62.26 | 59.96 | 54.42 | 83.34 | 74.05 | 72.24 | 56.26 | 90.18 | 76.05 | |
WT_IACAS_task2_2 | WTIACAS2025 | 44 | 57.44073696264025 ± 0.0013512115891784388 | 87.27 | 72.74 | 66.85 | 61.37 | 50.12 | 51.63 | 50.36 | 52.89 | 57.79 | 56.63 | 66.48 | 62.84 | 59.16 | 55.00 | 42.32 | 51.21 | 70.94 | 59.84 | 77.96 | 58.84 | 69.98 | 63.11 | 61.70 | 55.42 | 83.20 | 74.26 | 71.78 | 55.74 | 91.24 | 85.53 | |
WT_IACAS_task2_3 | WTIACAS2025 | 86 | 53.52111771682423 ± 0.001301200447333405 | 69.92 | 64.84 | 60.59 | 57.74 | 50.11 | 51.95 | 50.48 | 52.79 | 52.69 | 55.21 | 52.31 | 53.26 | 53.84 | 54.63 | 41.53 | 50.89 | 67.88 | 54.68 | 78.26 | 58.11 | 69.36 | 62.74 | 59.96 | 53.63 | 81.38 | 71.11 | 71.84 | 56.32 | 87.44 | 72.37 | |
WT_IACAS_task2_4 | WTIACAS2025 | 76 | 54.440119451520474 ± 0.001310278439474516 | 72.73 | 66.32 | 60.53 | 58.89 | 48.68 | 51.63 | 50.11 | 53.16 | 52.99 | 54.26 | 53.48 | 55.53 | 56.89 | 56.42 | 43.96 | 52.32 | 70.86 | 58.33 | 78.72 | 59.21 | 71.20 | 63.00 | 62.52 | 55.63 | 80.44 | 71.84 | 71.02 | 56.26 | 86.76 | 76.89 | |
Zhou_XAUAT_task2_1 | ZhouXAUAT2025 | 24 | 58.1881454965969 ± 0.001303450752882116 | 90.58 | 79.16 | 55.62 | 50.63 | 55.14 | 51.84 | 62.03 | 55.32 | 62.74 | 54.05 | 52.52 | 51.42 | 61.85 | 53.58 | 51.29 | 54.79 | 63.90 | 53.58 | 71.25 | 52.89 | 63.25 | 59.11 | 63.60 | 61.32 | 73.87 | 57.00 | 63.82 | 52.16 | 79.53 | 68.16 | |
Zhou_XAUAT_task2_2 | ZhouXAUAT2025 | 43 | 57.509962813993454 ± 0.0012796350724583706 | 90.53 | 79.68 | 53.19 | 50.21 | 56.85 | 52.89 | 63.58 | 54.63 | 60.72 | 53.63 | 49.32 | 51.21 | 58.37 | 54.63 | 52.05 | 53.63 | 65.48 | 53.37 | 66.67 | 50.53 | 64.05 | 60.26 | 64.58 | 59.84 | 70.63 | 54.68 | 62.03 | 52.32 | 73.25 | 58.89 | |
Zhou_XAUAT_task2_3 | ZhouXAUAT2025 | 95 | 52.66336603046757 ± 0.0011874412147661675 | 85.40 | 77.00 | 56.64 | 50.74 | 39.75 | 49.42 | 49.24 | 51.16 | 56.84 | 53.95 | 37.91 | 48.53 | 58.06 | 52.47 | 58.12 | 53.79 | 65.56 | 53.95 | 63.54 | 48.58 | 61.79 | 57.47 | 62.00 | 57.00 | 63.61 | 59.89 | 62.14 | 50.84 | 77.21 | 60.16 | |
Zhou_XAUAT_task2_4 | ZhouXAUAT2025 | 50 | 57.09208791484903 ± 0.001355863605136462 | 91.60 | 81.63 | 57.87 | 51.16 | 57.92 | 49.84 | 60.10 | 56.05 | 64.21 | 53.89 | 49.99 | 51.05 | 53.84 | 51.74 | 47.63 | 53.47 | 59.63 | 51.68 | 68.05 | 52.11 | 66.83 | 59.63 | 64.16 | 60.84 | 72.00 | 60.58 | 64.80 | 54.05 | 80.42 | 69.58 | |
Zhong_USTC_task2_1 | ZhongUSTC2025 | 54 | 56.89623750195928 ± 0.0013847743865767457 | 77.92 | 75.05 | 59.39 | 55.21 | 49.96 | 50.26 | 49.43 | 52.05 | 66.75 | 57.42 | 74.49 | 58.95 | 59.49 | 54.74 | 41.59 | 49.32 | 65.81 | 52.51 | 62.88 | 52.11 | 63.08 | 65.25 | 56.36 | 49.76 | 59.91 | 56.11 | 64.43 | 52.91 | 69.78 | 60.35 | |
Zhong_USTC_task2_2 | ZhongUSTC2025 | 51 | 57.068368795617666 ± 0.001397800488455209 | 81.32 | 56.10 | 59.39 | 55.21 | 49.96 | 50.26 | 49.43 | 52.05 | 70.54 | 62.16 | 76.01 | 63.53 | 59.49 | 54.74 | 41.59 | 49.32 | 64.70 | 49.98 | 61.41 | 52.80 | 64.25 | 63.50 | 59.37 | 51.23 | 60.67 | 57.53 | 62.92 | 54.99 | 70.83 | 60.66 | |
Zhong_USTC_task2_3 | ZhongUSTC2025 | 38 | 57.69762462723721 ± 0.0013868286522009086 | 77.92 | 75.05 | 55.96 | 55.21 | 58.76 | 55.68 | 49.43 | 52.05 | 66.75 | 57.42 | 74.49 | 58.95 | 59.49 | 54.74 | 41.59 | 49.32 | 64.52 | 53.14 | 62.48 | 52.97 | 64.53 | 65.64 | 55.95 | 52.13 | 58.55 | 58.48 | 64.58 | 56.25 | 69.82 | 59.29 | |
Zhong_USTC_task2_4 | ZhongUSTC2025 | 30 | 57.874646573614505 ± 0.0013941667172153518 | 81.32 | 56.10 | 55.96 | 55.21 | 58.76 | 55.68 | 49.43 | 52.05 | 70.54 | 62.16 | 76.01 | 63.53 | 59.49 | 54.74 | 41.59 | 49.32 | 67.49 | 54.24 | 62.69 | 52.67 | 60.26 | 63.58 | 55.37 | 49.68 | 60.16 | 56.24 | 62.44 | 57.98 | 68.88 | 61.13 | |
Vijayyan_SNUC_task2_1 | VijayyanSNUC2025 | 109 | 49.90505686153821 ± 0.0012811394826572587 | 64.39 | 56.74 | 41.16 | 48.32 | 45.70 | 50.53 | 34.37 | 47.37 | 53.20 | 51.32 | 72.65 | 63.47 | 51.10 | 52.89 | 48.03 | 49.89 | 63.24 | 49.05 | 62.76 | 54.32 | 65.14 | 58.32 | 57.58 | 51.58 | 63.38 | 54.74 | 64.14 | 53.05 | 81.16 | 63.16 | |
CHUNG_KUCAU_task2_1 | CHUNGKUCAU2025 | 97 | 52.53513672830565 ± 0.0011797106011080367 | 68.48 | 53.84 | 44.70 | 50.68 | 44.63 | 48.32 | 49.44 | 50.63 | 55.70 | 52.26 | 75.68 | 65.63 | 51.37 | 51.16 | 46.33 | 48.74 | 66.06 | 51.15 | 70.45 | 52.31 | 66.80 | 54.05 | 58.80 | 56.47 | 74.47 | 68.10 | 70.84 | 53.53 | 76.96 | 56.57 | |
CHUNG_KUCAU_task2_2 | CHUNGKUCAU2025 | 65 | 55.55388801652549 ± 0.0013144380478823914 | 77.77 | 66.42 | 55.63 | 53.63 | 54.64 | 55.84 | 56.95 | 54.26 | 54.23 | 51.63 | 57.98 | 51.37 | 55.47 | 50.16 | 47.72 | 48.84 | 65.38 | 52.68 | 71.20 | 56.57 | 68.85 | 52.42 | 57.30 | 55.36 | 69.75 | 54.36 | 69.42 | 53.57 | 74.34 | 58.63 | |
CHUNG_KUCAU_task2_3 | CHUNGKUCAU2025 | 72 | 54.77830306706913 ± 0.0012185601690566305 | 64.68 | 56.95 | 49.34 | 55.16 | 45.04 | 49.53 | 57.41 | 55.68 | 55.94 | 53.68 | 77.10 | 68.61 | 51.41 | 51.47 | 48.92 | 51.32 | 66.14 | 51.31 | 67.25 | 53.78 | 66.86 | 54.94 | 51.94 | 52.31 | 70.28 | 58.94 | 63.04 | 50.73 | 78.06 | 65.31 | |
CHUNG_KUCAU_task2_4 | CHUNGKUCAU2025 | 47 | 57.227161466047036 ± 0.0013105905782118228 | 77.48 | 67.68 | 67.33 | 56.95 | 48.55 | 51.16 | 63.55 | 57.84 | 56.18 | 52.37 | 74.23 | 67.32 | 47.25 | 47.58 | 46.82 | 51.37 | 66.81 | 51.36 | 70.31 | 558.78 | 72.06 | 57.00 | 51.06 | 51.31 | 61.06 | 52.57 | 65.06 | 50.15 | 78.40 | 67.78 | |
Dung_CNTT1PTIT_task2_1 | DungCNTT1PTIT2025 | 112 | 48.71892564275941 ± 0.0011303602632511981 | 38.71 | 54.58 | 50.06 | 48.68 | 54.41 | 51.74 | 51.51 | 51.05 | 51.62 | 50.58 | 43.46 | 49.11 | 43.03 | 48.68 | 54.54 | 52.47 | 48.32 | 52.63 | 50.39 | 50.26 | 52.78 | 53.95 | 52.22 | 50.58 | 50.47 | 63.32 | 46.94 | 51.21 | 46.73 | 49.53 | |
Zhang_NWPU_task2_1 | ZhangNWPU2025 | 103 | 51.63482668025466 ± 0.001309776511596873 | 41.53 | 52.42 | 64.58 | 55.47 | 52.83 | 50.11 | 57.37 | 58.00 | 62.86 | 56.42 | 58.46 | 55.58 | 57.11 | 58.74 | 31.15 | 48.42 | 66.74 | 49.47 | 75.10 | 53.11 | 72.30 | 61.05 | 62.70 | 59.84 | 77.40 | 60.11 | 76.92 | 58.47 | 83.70 | 58.72 | |
Zhang_NWPU_task2_2 | ZhangNWPU2025 | 67 | 55.42463764307989 ± 0.0013394739153165399 | 65.80 | 56.95 | 64.48 | 55.95 | 51.36 | 50.84 | 55.55 | 55.11 | 62.68 | 58.32 | 62.28 | 57.53 | 62.47 | 58.32 | 36.46 | 47.95 | 66.58 | 49.32 | 74.98 | 53.53 | 73.08 | 63.53 | 62.60 | 59.16 | 73.08 | 60.21 | 76.20 | 54.74 | 82.10 | 67.95 | |
Zhang_NWPU_task2_3 | ZhangNWPU2025 | 75 | 54.535850370502445 ± 0.0013342853613838863 | 63.40 | 57.68 | 64.20 | 54.68 | 51.38 | 51.05 | 55.59 | 56.00 | 62.95 | 59.26 | 62.17 | 57.47 | 61.29 | 58.42 | 33.08 | 47.63 | 65.98 | 49.26 | 75.04 | 53.26 | 72.34 | 62.47 | 63.10 | 60.37 | 75.42 | 57.89 | 75.48 | 56.32 | 82.62 | 70.16 | |
Zhang_NWPU_task2_4 | ZhangNWPU2025 | 82 | 54.27870089612588 ± 0.0013452482191526702 | 65.71 | 58.58 | 64.13 | 55.16 | 51.08 | 51.05 | 54.44 | 55.63 | 61.53 | 58.63 | 57.85 | 56.37 | 60.82 | 58.53 | 33.86 | 47.68 | 66.84 | 49.42 | 74.48 | 53.32 | 72.60 | 62.53 | 62.90 | 59.74 | 74.48 | 57.05 | 74.76 | 54.79 | 82.58 | 68.47 | |
Chao_BUCT_task2_1 | ChaoBUCT2025 | 118 | 47.24303918388332 ± 0.0012259541569508577 | 57.41 | 52.68 | 41.93 | 49.79 | 67.56 | 51.47 | 48.86 | 49.11 | 54.92 | 51.79 | 31.34 | 48.58 | 44.98 | 49.26 | 39.18 | 49.68 | 48.45 | 49.50 | 49.10 | 50.90 | 53.70 | 54.10 | 54.30 | 50.20 | 68.65 | 47.70 | 54.85 | 50.40 | 59.15 | 47.70 | |
Chao_BUCT_task2_2 | ChaoBUCT2025 | 114 | 48.45174273659997 ± 0.001176212566092576 | 53.46 | 48.92 | 44.62 | 50.69 | 42.21 | 48.91 | 37.75 | 51.92 | 53.29 | 52.39 | 61.71 | 51.62 | 50.96 | 49.23 | 44.67 | 49.65 | 55.50 | 50.40 | 48.50 | 48.70 | 56.00 | 55.70 | 53.00 | 50.30 | 49.50 | 49.90 | 49.00 | 49.10 | 77.00 | 62.90 | |
Chao_BUCT_task2_3 | ChaoBUCT2025 | 108 | 50.13154697532129 ± 0.001259520077091061 | 56.74 | 50.26 | 37.40 | 48.58 | 70.57 | 50.95 | 60.64 | 52.11 | 52.78 | 51.79 | 41.29 | 48.63 | 51.41 | 48.47 | 45.98 | 49.63 | 48.48 | 42.68 | 54.16 | 53.70 | 58.36 | 58.13 | 52.54 | 51.92 | 49.20 | 47.76 | 52.73 | 52.69 | 52.47 | 52.64 | |
Li_XJTLU_task2_1 | LiXJTLU2025 | 89 | 53.298245047211815 ± 0.0012918346317037904 | 76.28 | 68.11 | 48.51 | 51.53 | 42.51 | 52.05 | 55.24 | 50.05 | 60.21 | 50.95 | 54.35 | 50.05 | 52.99 | 51.00 | 50.95 | 49.79 | 68.73 | 53.05 | 69.35 | 55.10 | 58.22 | 50.78 | 50.65 | 54.21 | 59.23 | 51.78 | 63.95 | 51.10 | 75.81 | 66.99 | |
Li_XJTLU_task2_2 | LiXJTLU2025 | 87 | 53.469426047012206 ± 0.0012893785514650324 | 76.28 | 68.11 | 52.38 | 51.11 | 42.51 | 52.05 | 53.29 | 53.63 | 52.59 | 50.00 | 54.35 | 50.05 | 52.99 | 51.00 | 56.24 | 49.89 | 68.73 | 53.05 | 66.72 | 57.30 | 58.16 | 50.68 | 57.63 | 48.15 | 55.83 | 54.00 | 60.24 | 52.73 | 75.80 | 64.73 | |
Li_XJTLU_task2_3 | LiXJTLU2025 | 106 | 50.87625772429204 ± 0.0012462737985353197 | 67.74 | 57.74 | 46.34 | 51.63 | 48.25 | 52.37 | 46.24 | 48.37 | 46.78 | 49.95 | 45.75 | 48.37 | 58.63 | 54.42 | 52.16 | 50.26 | 66.31 | 51.52 | 62.82 | 51.78 | 48.73 | 52.47 | 52.37 | 48.36 | 57.37 | 52.36 | 59.50 | 49.57 | 77.25 | 69.78 | |
Li_XJTLU_task2_4 | LiXJTLU2025 | 99 | 52.52280596314458 ± 0.0012961572509312515 | 63.85 | 58.16 | 48.59 | 52.42 | 37.24 | 48.42 | 47.82 | 50.63 | 53.54 | 49.21 | 78.03 | 68.00 | 56.93 | 54.42 | 48.80 | 50.63 | 63.63 | 51.84 | 73.35 | 59.85 | 51.35 | 55.26 | 53.96 | 48.78 | 63.96 | 52.68 | 56.44 | 51.15 | 78.47 | 67.63 | |
Wang_ZJU_task2_1 | WangZJU2025 | 107 | 50.2293951333291 ± 0.0012299324269494255 | 33.40 | 48.53 | 52.12 | 52.16 | 52.76 | 51.95 | 45.32 | 51.21 | 55.14 | 52.11 | 58.16 | 60.37 | 47.52 | 54.95 | 58.06 | 56.16 | 56.80 | 60.16 | 60.29 | 60.16 | 58.08 | 52.88 | 58.80 | 53.25 | 60.87 | 49.32 | 58.90 | 50.37 | 55.99 | 52.05 | |
Wang_ZJU_task2_2 | WangZJU2025 | 64 | 55.58902163976555 ± 0.001199770971668403 | 61.28 | 53.32 | 47.72 | 50.89 | 50.59 | 50.37 | 49.31 | 51.58 | 60.14 | 53.68 | 74.77 | 68.47 | 56.40 | 55.00 | 58.76 | 53.95 | 56.62 | 54.57 | 64.96 | 53.21 | 58.03 | 52.88 | 60.69 | 48.82 | 43.75 | 48.26 | 55.14 | 53.21 | 56.48 | 52.85 | |
Wang_ZJU_task2_3 | WangZJU2025 | 85 | 53.7625220777451 ± 0.0012643280566019437 | 48.59 | 53.16 | 51.56 | 52.16 | 55.92 | 54.05 | 52.87 | 53.42 | 54.12 | 50.84 | 62.95 | 60.74 | 46.64 | 54.63 | 59.11 | 56.21 | 61.01 | 51.89 | 51.85 | 50.84 | 56.13 | 51.11 | 55.21 | 50.05 | 54.29 | 51.32 | 52.33 | 49.37 | 46.75 | 26.08 | |
Wang_ZJU_task2_4 | WangZJU2025 | 55 | 56.866003688314095 ± 0.001245781793743034 | 62.41 | 54.53 | 47.02 | 50.05 | 53.62 | 51.84 | 55.58 | 52.32 | 59.04 | 55.00 | 72.29 | 67.58 | 59.15 | 57.00 | 59.98 | 54.84 | 55.38 | 60.16 | 43.75 | 50.37 | 56.36 | 52.88 | 58.27 | 60.16 | 62.12 | 54.57 | 61.64 | 51.84 | 54.34 | 64.68 | |
Lin_IASP_task2_1 | LinIASP2025 | 105 | 51.07645561603922 ± 0.0011762821273072503 | 56.96 | 49.47 | 41.81 | 51.32 | 51.43 | 52.89 | 55.52 | 50.84 | 47.70 | 51.05 | 55.13 | 52.89 | 50.93 | 51.89 | 52.88 | 48.32 | 64.72 | 51.70 | 64.45 | 52.33 | 62.50 | 59.98 | 61.28 | 52.46 | 50.23 | 54.61 | 60.94 | 51.73 | 63.55 | 50.99 | |
Lin_IASP_task2_2 | LinIASP2025 | 78 | 54.41975154704433 ± 0.001185974404419855 | 83.90 | 73.00 | 47.76 | 51.16 | 45.70 | 50.11 | 50.34 | 49.42 | 58.62 | 53.05 | 60.02 | 58.79 | 49.50 | 52.84 | 54.80 | 51.95 | 64.72 | 51.70 | 64.45 | 52.33 | 62.50 | 59.98 | 61.28 | 52.46 | 50.23 | 54.61 | 60.94 | 51.73 | 63.55 | 50.99 | |
Lin_IASP_task2_3 | LinIASP2025 | 74 | 54.5484232027357 ± 0.0012131329471837765 | 82.83 | 71.37 | 41.81 | 51.32 | 51.93 | 48.68 | 48.79 | 53.42 | 58.62 | 53.05 | 60.98 | 61.89 | 47.54 | 53.05 | 58.01 | 54.32 | 64.72 | 51.70 | 64.45 | 52.33 | 62.50 | 59.98 | 61.28 | 52.46 | 50.23 | 54.61 | 60.94 | 51.73 | 63.55 | 50.99 | |
Lin_IASP_task2_4 | LinIASP2025 | 73 | 54.776114357402165 ± 0.0011741700736731956 | 77.88 | 69.58 | 47.76 | 51.16 | 52.12 | 51.42 | 48.18 | 49.74 | 53.30 | 53.26 | 61.41 | 58.74 | 54.74 | 54.68 | 54.49 | 50.47 | 59.73 | 50.67 | 60.99 | 49.68 | 62.39 | 59.73 | 59.72 | 55.78 | 60.18 | 57.47 | 60.20 | 53.15 | 67.18 | 59.38 | |
Lobanov_ITMO_task2_1 | LobanovITMO2025 | 110 | 49.58100152202029 ± 0.0011822233515733215 | 40.75 | 49.84 | 52.87 | 51.37 | 47.16 | 51.95 | 50.90 | 50.53 | 56.21 | 51.11 | 49.33 | 49.58 | 43.50 | 49.58 | 54.86 | 54.32 | 44.58 | 44.58 | 66.14 | 66.14 | 49.50 | 49.50 | 55.32 | 55.32 | 55.50 | 55.50 | 47.92 | 47.92 | 50.68 | 51.68 | |
Lobanov_ITMO_task2_2 | LobanovITMO2025 | 98 | 52.52595001212435 ± 0.001248375343145035 | 66.47 | 62.26 | 58.00 | 49.89 | 43.83 | 51.74 | 57.19 | 52.89 | 52.98 | 50.42 | 52.88 | 49.74 | 47.93 | 51.21 | 49.63 | 49.16 | 42.54 | 2550.00 | 63.52 | 3988.00 | 52.66 | 57.22 | 55.26 | 55.10 | 53.70 | 54.12 | 43.84 | 45.07 | 65.80 | 65.80 | |
Qian_nivic_task2_1 | Qiannivic2025 | 32 | 57.83526242732236 ± 0.001312869954266259 | 81.16 | 76.00 | 57.63 | 53.47 | 56.22 | 51.58 | 46.67 | 53.32 | 64.50 | 57.21 | 72.07 | 58.47 | 59.28 | 54.53 | 48.30 | 49.11 | 64.91 | 50.36 | 63.45 | 53.70 | 64.20 | 65.43 | 58.55 | 52.64 | 61.70 | 54.76 | 62.59 | 56.57 | 68.28 | 61.18 | |
Qian_nivic_task2_2 | Qiannivic2025 | 28 | 58.01974104378345 ± 0.0014035588713639333 | 82.51 | 56.10 | 57.63 | 53.47 | 56.22 | 51.58 | 46.67 | 53.32 | 68.13 | 61.26 | 75.28 | 64.47 | 59.28 | 54.53 | 48.30 | 49.11 | 64.23 | 51.53 | 60.88 | 55.30 | 63.58 | 65.32 | 56.81 | 50.22 | 59.73 | 56.21 | 63.31 | 53.87 | 69.04 | 59.39 | |
Qian_nivic_task2_3 | Qiannivic2025 | 49 | 57.129551545366276 ± 0.0013938679187242284 | 80.45 | 74.32 | 57.63 | 53.47 | 56.22 | 51.58 | 46.51 | 51.53 | 63.44 | 58.84 | 68.32 | 52.00 | 56.49 | 53.05 | 49.87 | 50.95 | 65.59 | 50.06 | 63.96 | 53.24 | 63.22 | 64.61 | 55.97 | 50.82 | 59.56 | 56.19 | 62.48 | 58.19 | 67.67 | 61.87 | |
Qian_nivic_task2_4 | Qiannivic2025 | 39 | 57.66599978958733 ± 0.001406927412209501 | 81.68 | 56.10 | 57.63 | 53.47 | 56.22 | 51.58 | 46.51 | 51.53 | 67.26 | 63.37 | 72.62 | 62.26 | 56.49 | 53.05 | 49.87 | 50.95 | 65.94 | 52.43 | 59.84 | 55.76 | 62.71 | 64.06 | 57.77 | 51.97 | 62.06 | 56.26 | 62.47 | 56.70 | 70.52 | 61.85 | |
Wang_MYPS_task2_1 | WangMYPS2025 | 16 | 59.265578571777176 ± 0.001423556951490758 | 80.10 | 72.00 | 57.48 | 50.79 | 58.90 | 53.21 | 47.15 | 49.47 | 65.28 | 51.79 | 94.24 | 85.32 | 60.99 | 53.58 | 46.61 | 52.68 | 67.27 | 51.84 | 62.54 | 53.64 | 62.81 | 67.03 | 56.33 | 51.85 | 59.66 | 57.60 | 61.05 | 55.30 | 69.09 | 60.66 | |
Wang_MYPS_task2_2 | WangMYPS2025 | 20 | 58.80363660036158 ± 0.001420353067640853 | 83.60 | 56.10 | 57.48 | 50.79 | 58.90 | 53.21 | 47.15 | 49.47 | 68.51 | 55.16 | 91.15 | 71.87 | 60.99 | 53.58 | 46.61 | 52.68 | 65.06 | 51.33 | 64.67 | 50.24 | 64.36 | 63.16 | 58.05 | 54.61 | 60.21 | 56.11 | 60.39 | 54.34 | 68.68 | 57.39 | |
Wang_MYPS_task2_3 | WangMYPS2025 | 1 | 61.62755928284949 ± 0.0013535025717832298 | 80.61 | 77.05 | 64.22 | 51.63 | 57.94 | 52.16 | 62.45 | 53.79 | 68.76 | 54.05 | 90.24 | 79.16 | 62.64 | 54.05 | 44.35 | 52.84 | 66.22 | 50.74 | 62.10 | 54.63 | 62.41 | 65.68 | 56.98 | 51.60 | 60.18 | 57.75 | 62.34 | 53.08 | 68.66 | 62.28 | |
Wang_MYPS_task2_4 | WangMYPS2025 | 4 | 61.04162543834627 ± 0.0014587410270351347 | 83.98 | 56.10 | 64.22 | 51.63 | 57.94 | 52.16 | 62.45 | 53.79 | 72.62 | 58.47 | 87.34 | 67.89 | 62.64 | 54.05 | 44.35 | 52.84 | 65.33 | 51.54 | 63.27 | 54.07 | 63.58 | 65.39 | 58.05 | 51.76 | 61.39 | 54.39 | 60.31 | 54.30 | 69.78 | 58.53 | |
Emon_HDK_task2_1 | EmonHDK2025 | 121 | 45.15099353175931 ± 0.0012413329586015724 | 30.49 | 52.26 | 35.37 | 48.47 | 70.66 | 52.21 | 46.39 | 52.68 | 43.90 | 49.58 | 53.52 | 48.58 | 37.77 | 48.32 | 46.00 | 51.84 | 72.55 | 65.40 | 90.05 | 83.60 | 94.20 | 85.80 | 67.35 | 67.50 | 86.35 | 50.80 | 92.15 | 70.30 | 85.95 | 61.40 | |
Fu_CUMT_task2_1 | FuCUMT2025 | 52 | 57.0225500546113 ± 0.001410191721154638 | 80.53 | 77.16 | 57.22 | 56.11 | 54.84 | 50.79 | 48.92 | 49.32 | 64.55 | 58.21 | 70.00 | 52.79 | 55.68 | 55.37 | 46.10 | 50.11 | 63.70 | 52.16 | 62.30 | 53.41 | 61.71 | 61.33 | 56.37 | 51.05 | 61.18 | 55.03 | 62.23 | 56.09 | 67.24 | 57.43 | |
Fu_CUMT_task2_2 | FuCUMT2025 | 40 | 57.58455696377345 ± 0.0014509734792130385 | 82.07 | 56.10 | 57.22 | 56.11 | 54.84 | 50.79 | 48.92 | 49.32 | 69.41 | 62.95 | 75.01 | 63.26 | 55.68 | 55.37 | 46.10 | 50.11 | 63.40 | 51.75 | 61.55 | 54.80 | 61.44 | 65.71 | 58.05 | 53.52 | 58.87 | 55.54 | 62.37 | 56.47 | 68.78 | 59.74 | |
Fu_CUMT_task2_3 | FuCUMT2025 | 41 | 57.56389841892947 ± 0.0014244611227164524 | 80.53 | 77.16 | 55.06 | 54.95 | 60.47 | 57.53 | 48.92 | 49.32 | 64.55 | 58.21 | 70.00 | 52.79 | 55.68 | 55.37 | 46.10 | 50.11 | 65.77 | 54.18 | 60.35 | 50.20 | 62.01 | 65.83 | 56.58 | 50.85 | 60.75 | 55.54 | 63.00 | 53.78 | 65.53 | 59.04 | |
Fu_CUMT_task2_4 | FuCUMT2025 | 26 | 58.13668049404887 ± 0.0014644138554073664 | 82.07 | 56.10 | 55.06 | 54.95 | 60.47 | 57.53 | 48.92 | 49.32 | 69.41 | 62.95 | 75.01 | 63.26 | 55.68 | 55.37 | 46.10 | 50.11 | 65.22 | 54.44 | 62.06 | 53.99 | 61.73 | 63.65 | 58.73 | 54.06 | 62.35 | 53.25 | 61.84 | 54.52 | 68.70 | 61.88 | |
Ding_HFUU_task2_1 | DingHFUU2025 | 71 | 55.13017039561223 ± 0.0012660750733118095 | 70.25 | 65.89 | 44.77 | 51.00 | 52.35 | 51.53 | 51.44 | 55.16 | 60.23 | 54.05 | 66.89 | 56.89 | 46.79 | 53.47 | 59.11 | 54.58 | 71.81 | 50.63 | 64.14 | 53.36 | 58.84 | 57.58 | 59.72 | 55.57 | 55.58 | 53.15 | 72.11 | 55.57 | 50.73 | 50.31 | |
Ding_HFUU_task2_2 | DingHFUU2025 | 81 | 54.30344227814425 ± 0.0012121148394503214 | 65.44 | 60.74 | 42.99 | 50.21 | 56.81 | 50.89 | 52.93 | 55.05 | 56.89 | 54.26 | 71.94 | 58.47 | 44.85 | 51.37 | 55.38 | 52.58 | 72.96 | 53.68 | 62.54 | 53.68 | 60.08 | 52.94 | 60.94 | 52.10 | 56.66 | 51.78 | 72.96 | 54.63 | 54.46 | 52.21 | |
Ding_HFUU_task2_3 | DingHFUU2025 | 91 | 53.15844555398661 ± 0.001265833986842346 | 46.64 | 52.42 | 51.76 | 51.58 | 54.65 | 52.84 | 54.00 | 52.68 | 53.21 | 50.79 | 61.60 | 59.47 | 47.12 | 54.89 | 57.49 | 55.74 | 62.29 | 49.89 | 58.17 | 50.69 | 55.02 | 60.63 | 54.66 | 49.84 | 57.48 | 53.10 | 59.26 | 51.73 | 64.73 | 57.11 | |
Ding_HFUU_task2_4 | DingHFUU2025 | 59 | 56.37774443771024 ± 0.001224450673236013 | 65.98 | 51.95 | 46.57 | 49.47 | 53.42 | 52.11 | 52.68 | 52.21 | 58.71 | 53.89 | 70.26 | 67.84 | 59.27 | 57.32 | 59.16 | 54.32 | 62.80 | 49.70 | 58.81 | 50.19 | 58.94 | 58.12 | 58.55 | 61.05 | 61.86 | 50.63 | 61.67 | 55.05 | 64.66 | 57.35 | |
Yang_NBU_task2_1 | YangNBU2025 | 3 | 61.20134568401735 ± 0.0013249535881398125 | 84.76 | 75.37 | 66.41 | 57.79 | 50.09 | 49.63 | 62.99 | 53.37 | 75.64 | 61.32 | 94.22 | 80.00 | 56.83 | 53.21 | 42.80 | 49.74 | 64.06 | 51.79 | 61.85 | 55.90 | 63.34 | 63.56 | 58.19 | 49.97 | 59.95 | 53.25 | 63.96 | 56.00 | 68.71 | 58.42 | |
Yang_NBU_task2_2 | YangNBU2025 | 6 | 60.44696174623352 ± 0.0014213546943424226 | 84.52 | 56.10 | 66.41 | 57.79 | 50.09 | 49.63 | 62.99 | 53.37 | 78.59 | 65.84 | 91.62 | 70.03 | 56.83 | 53.21 | 42.80 | 49.74 | 66.91 | 51.47 | 62.55 | 52.49 | 62.19 | 66.72 | 59.88 | 49.85 | 60.66 | 54.93 | 62.14 | 54.12 | 69.23 | 57.53 | |
Yang_NBU_task2_3 | YangNBU2025 | 5 | 60.950192566179204 ± 0.0013762983100007208 | 83.59 | 80.21 | 64.34 | 55.21 | 55.03 | 51.32 | 64.62 | 53.58 | 75.46 | 60.58 | 92.60 | 80.79 | 55.42 | 53.63 | 39.19 | 50.05 | 64.70 | 53.55 | 60.22 | 51.57 | 61.95 | 66.22 | 57.96 | 51.96 | 60.96 | 55.16 | 61.89 | 56.53 | 68.79 | 60.40 | |
Yang_NBU_task2_4 | YangNBU2025 | 8 | 59.924392523484435 ± 0.0013902482612744536 | 83.76 | 56.10 | 64.34 | 55.21 | 55.03 | 51.32 | 64.62 | 53.58 | 77.39 | 63.89 | 89.56 | 68.53 | 55.42 | 53.63 | 39.19 | 50.05 | 67.08 | 50.26 | 61.55 | 55.83 | 63.87 | 66.92 | 58.16 | 49.70 | 61.31 | 54.53 | 61.99 | 57.06 | 71.15 | 58.99 | |
Kret_CU_task2_1 | KretCU2025 | 115 | 47.90299388774425 ± 0.001155154341464739 | 32.60 | 48.42 | 50.35 | 50.32 | 45.49 | 49.26 | 33.00 | 49.63 | 51.97 | 51.21 | 61.04 | 62.79 | 56.35 | 54.74 | 59.08 | 49.79 | 47.78 | 0.00 | 56.94 | 13.60 | 50.76 | 13.20 | 51.64 | 0.80 | 58.10 | 0.00 | 56.12 | 5.20 | 55.00 | 3.60 | |
Zheng_SJTU-AITHU_task2_1 | ZhengSJTU-AITHU2025 | 13 | 59.36979601971116 ± 0.0013888188480768546 | 91.32 | 79.74 | 68.08 | 58.53 | 52.22 | 47.53 | 57.61 | 55.58 | 60.82 | 54.74 | 65.11 | 51.26 | 63.34 | 57.00 | 48.08 | 52.11 | 65.52 | 51.79 | 75.23 | 62.11 | 68.11 | 60.68 | 61.97 | 55.42 | 78.08 | 68.79 | 82.31 | 60.63 | 91.04 | 78.11 | |
Zheng_SJTU-AITHU_task2_2 | ZhengSJTU-AITHU2025 | 11 | 59.49972264254595 ± 0.0013491175199334372 | 94.60 | 85.58 | 68.09 | 58.95 | 52.27 | 47.58 | 56.31 | 55.32 | 58.22 | 54.37 | 67.21 | 53.21 | 64.23 | 56.00 | 47.46 | 52.95 | 66.15 | 53.16 | 73.63 | 57.63 | 67.60 | 58.79 | 61.96 | 55.89 | 82.57 | 67.53 | 84.90 | 59.95 | 90.82 | 84.11 | |
Zheng_SJTU-AITHU_task2_3 | ZhengSJTU-AITHU2025 | 15 | 59.31413929500593 ± 0.00135439984680855 | 95.21 | 86.79 | 67.85 | 57.89 | 51.25 | 47.42 | 56.21 | 55.05 | 57.84 | 54.68 | 66.53 | 53.21 | 63.62 | 55.89 | 48.21 | 52.74 | 65.94 | 52.84 | 72.85 | 56.53 | 66.90 | 58.00 | 61.90 | 55.42 | 83.02 | 67.79 | 84.61 | 59.68 | 90.46 | 84.63 | |
Zheng_SJTU-AITHU_task2_4 | ZhengSJTU-AITHU2025 | 12 | 59.44141398288725 ± 0.0013473754932008504 | 94.66 | 85.16 | 67.95 | 58.47 | 52.35 | 47.58 | 56.57 | 55.00 | 57.82 | 54.37 | 67.06 | 53.58 | 64.01 | 55.53 | 47.65 | 52.95 | 66.15 | 53.11 | 73.74 | 57.47 | 67.32 | 58.11 | 61.71 | 55.79 | 82.70 | 67.53 | 85.02 | 60.05 | 90.74 | 84.11 | |
Zhao_CUMT_task2_1 | ZhaoCUMT2025 | 102 | 51.65518476016675 ± 0.0011614665718661544 | 65.29 | 61.42 | 53.11 | 51.95 | 42.88 | 49.26 | 51.70 | 49.11 | 42.34 | 48.47 | 51.53 | 52.00 | 61.41 | 54.95 | 51.92 | 51.00 | 61.02 | 52.28 | 68.78 | 51.98 | 71.44 | 55.16 | 54.66 | 52.51 | 77.94 | 54.02 | 62.74 | 54.07 | 87.86 | 53.71 | |
Zhao_CUMT_task2_2 | ZhaoCUMT2025 | 101 | 52.09351496965263 ± 0.0011749337979479683 | 61.95 | 61.26 | 50.66 | 50.84 | 56.21 | 49.53 | 43.86 | 49.74 | 47.85 | 50.11 | 52.72 | 51.11 | 58.24 | 55.68 | 50.21 | 49.79 | 60.74 | 52.50 | 67.40 | 52.14 | 68.30 | 55.16 | 55.14 | 52.63 | 75.12 | 53.88 | 65.28 | 53.50 | 85.30 | 54.49 | |
Zhao_CUMT_task2_3 | ZhaoCUMT2025 | 61 | 56.05710040362136 ± 0.0013546424429837578 | 59.85 | 51.05 | 48.25 | 50.89 | 51.73 | 50.26 | 51.71 | 53.37 | 62.63 | 56.84 | 80.06 | 68.63 | 51.79 | 54.26 | 59.71 | 54.21 | 60.14 | 48.84 | 48.57 | 48.74 | 61.26 | 63.21 | 55.85 | 50.32 | 61.96 | 54.53 | 61.93 | 52.58 | 54.28 | 51.31 | |
Zhao_CUMT_task2_4 | ZhaoCUMT2025 | 62 | 55.78430154315296 ± 0.0012150708201156357 | 63.72 | 51.95 | 51.26 | 51.63 | 49.47 | 51.37 | 52.51 | 52.42 | 57.15 | 51.63 | 69.66 | 66.37 | 56.50 | 55.74 | 58.57 | 54.00 | 57.65 | 49.89 | 44.92 | 48.26 | 59.90 | 61.84 | 52.52 | 51.84 | 60.70 | 52.68 | 60.66 | 53.53 | 54.55 | 51.68 | |
Ozeki_MELCO_task2_1 | OzekiMELCO2025 | 23 | 58.23359688192865 ± 0.0011667802614379898 | 86.35 | 74.79 | 62.04 | 55.79 | 46.36 | 51.05 | 56.50 | 50.63 | 62.40 | 55.63 | 52.30 | 49.42 | 64.14 | 56.89 | 62.21 | 55.11 | 58.16 | 51.52 | 71.56 | 49.68 | 56.63 | 53.10 | 54.53 | 53.73 | 61.90 | 56.94 | 73.65 | 59.52 | 82.78 | 73.26 | |
Ozeki_MELCO_task2_2 | OzekiMELCO2025 | 60 | 56.12173861798142 ± 0.0011524809967321202 | 86.24 | 71.00 | 57.86 | 53.58 | 43.39 | 48.95 | 54.66 | 50.42 | 60.31 | 55.53 | 51.94 | 49.74 | 64.07 | 54.79 | 56.06 | 51.89 | 61.42 | 52.10 | 70.91 | 50.68 | 63.04 | 56.31 | 55.81 | 54.89 | 63.41 | 55.68 | 71.11 | 60.21 | 81.73 | 72.31 | |
Ozeki_MELCO_task2_3 | OzekiMELCO2025 | 69 | 55.29605114517713 ± 0.0011605518223287337 | 85.94 | 70.26 | 57.86 | 53.58 | 40.04 | 49.74 | 52.02 | 48.37 | 60.31 | 55.53 | 51.94 | 49.74 | 64.09 | 55.00 | 56.06 | 51.89 | 62.12 | 51.78 | 70.79 | 50.78 | 63.56 | 56.42 | 55.59 | 54.89 | 63.55 | 56.31 | 71.10 | 60.63 | 82.15 | 74.47 | |
Ozeki_MELCO_task2_4 | OzekiMELCO2025 | 79 | 54.40720942473768 ± 0.001159342857416225 | 73.37 | 58.00 | 61.00 | 53.63 | 47.47 | 52.58 | 55.19 | 50.63 | 60.11 | 53.68 | 46.61 | 49.11 | 60.06 | 50.00 | 49.65 | 51.84 | 55.11 | 50.84 | 68.48 | 55.05 | 62.69 | 61.47 | 54.14 | 49.52 | 53.06 | 53.63 | 67.54 | 55.89 | 81.89 | 72.47 | |
Huang_XJU_task2_1 | HuangXJU2025 | 25 | 58.14052430128241 ± 0.0012193313166606692 | 74.01 | 53.63 | 57.15 | 59.37 | 46.98 | 49.21 | 65.68 | 52.79 | 52.08 | 50.42 | 82.14 | 69.32 | 61.78 | 57.68 | 54.54 | 51.00 | 66.22 | 53.63 | 72.08 | 56.16 | 75.86 | 64.95 | 59.20 | 50.58 | 72.02 | 58.32 | 65.55 | 54.21 | 76.20 | 74.74 | |
Huang_XJU_task2_2 | HuangXJU2025 | 27 | 58.06980771137975 ± 0.0012591353671909718 | 57.90 | 49.37 | 56.38 | 56.05 | 68.50 | 51.63 | 62.25 | 51.89 | 51.69 | 50.32 | 78.44 | 63.11 | 59.37 | 59.05 | 56.31 | 52.16 | 64.63 | 50.21 | 72.52 | 58.16 | 67.98 | 52.58 | 57.48 | 50.63 | 74.98 | 60.42 | 67.23 | 51.21 | 73.25 | 60.21 | |
Huang_XJU_task2_3 | HuangXJU2025 | 35 | 57.73907728732644 ± 0.0012365379072278196 | 69.81 | 52.26 | 58.29 | 56.16 | 48.99 | 49.42 | 63.25 | 51.95 | 52.93 | 49.95 | 81.05 | 65.68 | 58.36 | 58.32 | 55.21 | 54.11 | 67.75 | 51.84 | 76.45 | 62.21 | 71.90 | 62.74 | 57.38 | 51.16 | 71.41 | 57.68 | 64.08 | 52.26 | 76.55 | 62.47 | |
Huang_XJU_task2_4 | HuangXJU2025 | 37 | 57.70644391486902 ± 0.0012888105762262749 | 49.97 | 48.53 | 58.08 | 58.68 | 68.10 | 52.21 | 61.42 | 52.26 | 54.04 | 50.95 | 80.49 | 68.11 | 60.10 | 57.00 | 53.46 | 52.16 | 66.74 | 54.05 | 75.42 | 56.37 | 72.04 | 61.58 | 58.75 | 52.21 | 68.68 | 59.84 | 64.14 | 52.95 | 79.50 | 70.21 | |
Fujimura_NU_task2_1 | FujimuraNU2025 | 7 | 59.99466314801132 ± 0.001325555756111588 | 78.10 | 65.53 | 74.27 | 61.84 | 50.36 | 52.00 | 64.66 | 53.68 | 61.79 | 58.47 | 83.84 | 68.89 | 57.33 | 57.05 | 41.64 | 51.89 | 67.08 | 48.63 | 72.48 | 56.79 | 77.74 | 57.21 | 53.60 | 52.05 | 72.86 | 60.58 | 66.99 | 54.47 | 92.52 | 82.58 | |
Fujimura_NU_task2_2 | FujimuraNU2025 | 21 | 58.51049267405695 ± 0.0012020424314932898 | 83.69 | 61.11 | 68.46 | 58.95 | 48.96 | 50.89 | 62.91 | 55.21 | 52.05 | 51.37 | 82.32 | 66.11 | 57.29 | 58.00 | 45.19 | 51.00 | 66.12 | 49.05 | 72.67 | 55.74 | 62.39 | 58.32 | 55.98 | 52.95 | 70.81 | 56.84 | 65.44 | 51.21 | 85.18 | 73.63 | |
Fujimura_NU_task2_3 | FujimuraNU2025 | 14 | 59.343265980019446 ± 0.0012913235359995308 | 72.97 | 64.68 | 73.51 | 61.89 | 49.03 | 49.58 | 63.42 | 54.95 | 57.33 | 54.95 | 89.49 | 73.05 | 57.88 | 58.16 | 42.35 | 51.53 | 65.56 | 49.84 | 73.36 | 57.26 | 69.36 | 59.26 | 54.49 | 52.11 | 70.28 | 57.79 | 68.43 | 52.21 | 88.06 | 78.42 | |
Fujimura_NU_task2_4 | FujimuraNU2025 | 9 | 59.908156673117766 ± 0.0013659827543125128 | 75.57 | 65.00 | 71.96 | 60.84 | 49.00 | 50.89 | 64.67 | 55.26 | 60.50 | 55.00 | 86.90 | 67.95 | 57.38 | 58.89 | 44.39 | 52.16 | 66.97 | 49.68 | 73.87 | 57.37 | 77.19 | 61.16 | 54.14 | 51.95 | 71.26 | 59.32 | 67.89 | 50.89 | 90.68 | 79.58 | |
Jiang_THUEE_task2_1 | JiangTHUEE2025 | 19 | 58.89185629814288 ± 0.0012963285929123896 | 95.16 | 86.37 | 66.96 | 56.95 | 51.12 | 47.53 | 56.28 | 54.68 | 55.86 | 53.95 | 66.20 | 53.79 | 62.34 | 55.79 | 48.42 | 52.47 | 66.49 | 52.95 | 73.28 | 57.89 | 66.62 | 57.16 | 62.56 | 55.63 | 83.75 | 68.42 | 84.31 | 58.84 | 90.55 | 83.95 | |
Jiang_THUEE_task2_2 | JiangTHUEE2025 | 10 | 59.79329753333621 ± 0.0013522826832875893 | 94.98 | 86.21 | 67.95 | 58.26 | 52.66 | 47.37 | 54.77 | 55.89 | 59.64 | 54.68 | 70.12 | 56.89 | 64.66 | 56.37 | 46.94 | 51.47 | 66.90 | 53.00 | 72.71 | 55.95 | 68.93 | 60.58 | 61.91 | 55.00 | 81.95 | 68.32 | 85.56 | 60.26 | 92.04 | 86.37 | |
Jiang_THUEE_task2_3 | JiangTHUEE2025 | 18 | 59.07396719211147 ± 0.0013421239376490846 | 95.24 | 87.11 | 66.98 | 56.00 | 51.98 | 47.42 | 54.65 | 56.16 | 57.16 | 54.37 | 68.41 | 54.53 | 64.07 | 56.00 | 46.75 | 51.84 | 66.42 | 52.32 | 73.60 | 56.26 | 66.94 | 58.74 | 62.56 | 56.00 | 82.48 | 67.16 | 84.53 | 58.11 | 91.66 | 86.63 | |
Jiang_THUEE_task2_4 | JiangTHUEE2025 | 17 | 59.155503202726436 ± 0.0013452286475630093 | 95.77 | 87.58 | 66.38 | 56.32 | 51.73 | 47.37 | 54.22 | 55.63 | 57.79 | 54.21 | 69.27 | 55.68 | 63.56 | 55.74 | 47.12 | 52.21 | 66.65 | 52.53 | 72.92 | 56.11 | 67.53 | 59.42 | 62.62 | 56.05 | 83.01 | 69.00 | 84.70 | 58.53 | 91.55 | 86.53 | |
Bian_TGU_task2_1 | BianTGU2025 | 119 | 46.5757775343439 ± 0.0011764887428223725 | 47.32 | 50.21 | 47.21 | 49.47 | 44.21 | 48.63 | 39.33 | 48.37 | 45.02 | 51.05 | 47.52 | 48.68 | 48.66 | 52.42 | 42.78 | 50.95 | 43.45 | 49.63 | 48.67 | 49.47 | 60.57 | 65.44 | 48.76 | 49.94 | 56.79 | 54.05 | 49.87 | 51.42 | 55.25 | 49.26 | |
Bian_TGU_task2_2 | BianTGU2025 | 116 | 47.8506175553425 ± 0.0011826605625451974 | 41.69 | 49.00 | 39.99 | 50.32 | 49.01 | 51.42 | 49.81 | 49.16 | 49.53 | 50.95 | 47.99 | 52.53 | 53.37 | 49.74 | 45.61 | 49.05 | 42.91 | 49.47 | 46.47 | 49.10 | 53.55 | 55.66 | 51.26 | 50.26 | 44.05 | 51.84 | 57.75 | 52.58 | 47.56 | 49.10 | |
Bian_TGU_task2_3 | BianTGU2025 | 104 | 51.417480569726926 ± 0.0011698485309061142 | 44.25 | 49.79 | 55.13 | 50.89 | 49.26 | 51.05 | 57.35 | 51.63 | 46.36 | 48.16 | 57.37 | 51.84 | 52.45 | 51.68 | 55.26 | 51.53 | 48.09 | 48.52 | 46.53 | 49.63 | 49.90 | 49.16 | 52.63 | 50.94 | 52.80 | 49.53 | 55.24 | 51.16 | 48.80 | 51.74 | |
Bian_TGU_task2_4 | BianTGU2025 | 120 | 46.193399822616286 ± 0.0011813603654829486 | 49.08 | 49.79 | 37.42 | 49.58 | 39.25 | 47.58 | 48.87 | 49.74 | 44.87 | 48.95 | 45.85 | 50.11 | 46.73 | 52.16 | 48.49 | 49.26 | 50.82 | 50.42 | 53.76 | 49.58 | 57.15 | 54.26 | 48.18 | 49.94 | 52.66 | 49.00 | 50.15 | 49.84 | 44.24 | 47.95 | |
Sera_TMU_task2_1 | SeraTMU2025 | 57 | 56.567561042877735 ± 0.0013602097930091965 | 57.68 | 52.00 | 52.72 | 54.05 | 60.92 | 53.21 | 51.90 | 50.58 | 49.11 | 49.47 | 83.39 | 67.21 | 59.66 | 55.74 | 60.14 | 51.32 | 71.28 | 54.63 | 73.08 | 57.42 | 67.80 | 61.36 | 56.06 | 51.79 | 71.52 | 62.42 | 67.38 | 49.42 | 69.48 | 56.63 | |
Kim_DAU_task2_1 | KimDAU2025 | 113 | 48.536836193824676 ± 0.0011898932833159556 | 70.24 | 52.00 | 44.41 | 49.79 | 57.47 | 51.95 | 37.38 | 49.74 | 44.11 | 50.74 | 45.82 | 49.95 | 41.54 | 48.84 | 54.86 | 49.58 | 56.69 | 54.11 | 63.35 | 50.19 | 69.25 | 61.21 | 60.71 | 50.47 | 61.25 | 64.50 | 63.13 | 51.34 | 73.81 | 56.42 | |
Kim_DAU_task2_2 | KimDAU2025 | 111 | 48.96578012474582 ± 0.0011669517429796642 | 59.12 | 49.16 | 44.24 | 50.84 | 40.06 | 49.89 | 57.76 | 56.05 | 47.59 | 50.11 | 49.97 | 51.95 | 45.08 | 48.11 | 47.18 | 50.37 | 77.58 | 66.87 | 77.83 | 68.42 | 78.50 | 69.53 | 77.43 | 65.33 | 78.30 | 69.64 | 77.21 | 64.10 | 78.81 | 67.27 | |
Wang_UniS_task2_1 | WangUniS2025 | 34 | 57.754303274825524 ± 0.0012838059463938772 | 86.59 | 72.84 | 58.69 | 55.00 | 50.61 | 51.26 | 60.86 | 54.11 | 54.32 | 53.05 | 58.10 | 52.84 | 63.87 | 57.63 | 50.19 | 53.84 | 60.74 | 49.95 | 66.68 | 50.53 | 52.89 | 50.11 | 56.02 | 53.11 | 71.89 | 72.65 | 78.14 | 56.63 | 86.38 | 78.32 | |
Wang_UniS_task2_2 | WangUniS2025 | 46 | 57.26149732878484 ± 0.001272053328123361 | 84.68 | 69.79 | 58.72 | 56.63 | 48.88 | 51.53 | 60.96 | 55.58 | 54.66 | 53.05 | 59.08 | 51.63 | 63.96 | 57.37 | 47.65 | 52.63 | 59.74 | 44.95 | 66.06 | 50.72 | 53.42 | 50.23 | 55.94 | 53.55 | 72.21 | 72.31 | 78.17 | 55.19 | 85.99 | 78.34 | |
Wang_UniS_task2_3 | WangUniS2025 | 53 | 56.97485127107289 ± 0.001275596796553062 | 79.63 | 63.47 | 60.64 | 55.58 | 49.83 | 50.05 | 55.61 | 53.89 | 51.49 | 48.84 | 65.91 | 62.42 | 60.58 | 57.63 | 51.57 | 50.16 | 55.84 | 51.00 | 61.30 | 49.00 | 54.61 | 56.21 | 54.56 | 51.53 | 65.08 | 56.21 | 72.92 | 52.26 | 78.68 | 69.26 | |
Wang_UniS_task2_4 | WangUniS2025 | 90 | 53.19038024532819 ± 0.0012517639544326333 | 66.09 | 70.11 | 51.89 | 51.63 | 48.75 | 52.53 | 41.70 | 51.68 | 54.52 | 52.68 | 55.64 | 47.37 | 54.85 | 52.68 | 58.09 | 53.16 | 55.65 | 50.10 | 59.83 | 46.32 | 52.66 | 57.89 | 55.10 | 51.99 | 64.17 | 56.24 | 72.12 | 54.34 | 73.61 | 66.85 | |
Guan_HEU_task2_1 | GuanHEU2025 | 22 | 58.25344060002535 ± 0.0011865451404030904 | 70.81 | 59.53 | 55.51 | 53.63 | 46.55 | 49.58 | 49.38 | 50.74 | 61.83 | 56.11 | 89.98 | 77.26 | 62.19 | 61.79 | 55.60 | 51.89 | 72.19 | 54.65 | 75.44 | 57.42 | 64.71 | 61.51 | 56.81 | 53.06 | 70.24 | 57.79 | 67.58 | 53.80 | 73.35 | 65.50 | |
Guan_HEU_task2_2 | GuanHEU2025 | 48 | 57.2109893578078 ± 0.0011975016881285864 | 64.38 | 53.37 | 54.63 | 52.95 | 45.62 | 50.11 | 48.62 | 50.42 | 61.44 | 56.00 | 89.05 | 75.42 | 62.38 | 61.32 | 55.95 | 52.11 | 72.13 | 55.40 | 75.86 | 56.35 | 67.84 | 62.57 | 56.43 | 50.72 | 70.36 | 58.00 | 66.00 | 53.16 | 72.90 | 63.80 | |
Guan_HEU_task2_3 | GuanHEU2025 | 117 | 47.50449562562062 ± 0.0011856688491959603 | 34.21 | 49.42 | 42.01 | 52.11 | 38.63 | 49.47 | 60.67 | 55.63 | 48.78 | 49.84 | 66.97 | 56.74 | 45.84 | 50.05 | 44.82 | 50.00 | 77.67 | 61.56 | 68.48 | 50.24 | 59.41 | 52.21 | 62.32 | 52.21 | 72.50 | 59.70 | 68.00 | 52.47 | 85.68 | 72.46 | |
Guan_HEU_task2_4 | GuanHEU2025 | 68 | 55.31771382417022 ± 0.001333439788966255 | 57.04 | 50.00 | 51.24 | 54.53 | 41.12 | 49.74 | 53.34 | 52.26 | 59.76 | 55.63 | 88.68 | 72.84 | 58.88 | 59.11 | 52.73 | 51.26 | 77.06 | 60.71 | 74.96 | 57.36 | 67.07 | 60.29 | 58.73 | 51.09 | 72.11 | 62.52 | 67.29 | 52.79 | 80.62 | 73.15 | |
Kim_AISTAT_task2_1 | KimAISTAT2025 | 31 | 57.845411436558095 ± 0.0013619993164548112 | 89.97 | 77.47 | 54.93 | 54.74 | 50.89 | 55.47 | 70.06 | 61.32 | 50.74 | 51.11 | 59.19 | 52.00 | 64.41 | 55.68 | 47.54 | 50.47 | 66.12 | 51.00 | 75.40 | 60.42 | 66.02 | 56.16 | 62.14 | 52.00 | 73.96 | 66.53 | 82.60 | 64.63 | 78.82 | 71.58 | |
Kim_AISTAT_task2_2 | KimAISTAT2025 | 33 | 57.807994655715866 ± 0.001359971937463962 | 90.53 | 78.37 | 54.48 | 54.58 | 50.80 | 55.32 | 69.98 | 61.53 | 50.65 | 50.84 | 59.42 | 51.47 | 64.61 | 54.95 | 47.62 | 51.00 | 66.30 | 51.63 | 75.24 | 60.21 | 66.30 | 56.21 | 62.06 | 51.79 | 74.04 | 66.79 | 82.50 | 64.05 | 78.80 | 71.63 | |
Kim_AISTAT_task2_3 | KimAISTAT2025 | 36 | 57.732684643389376 ± 0.0013578163797103682 | 91.14 | 78.05 | 54.49 | 54.05 | 50.67 | 55.00 | 69.40 | 61.32 | 50.75 | 51.21 | 59.92 | 50.84 | 63.79 | 54.47 | 47.92 | 51.16 | 65.72 | 50.68 | 75.80 | 61.37 | 66.26 | 55.84 | 62.46 | 51.84 | 74.78 | 66.16 | 82.62 | 63.84 | 78.02 | 70.26 | |
Kim_AISTAT_task2_4 | KimAISTAT2025 | 29 | 57.95495072758183 ± 0.0013606493522197662 | 90.21 | 77.47 | 55.11 | 54.53 | 50.96 | 55.32 | 69.84 | 61.53 | 51.39 | 51.63 | 59.97 | 51.58 | 63.67 | 55.79 | 47.60 | 50.47 | 65.72 | 50.42 | 75.68 | 61.68 | 66.04 | 55.58 | 62.08 | 51.84 | 74.80 | 66.89 | 82.20 | 63.79 | 78.12 | 70.42 |
Supplementary metrics (recall, precision, and F1 score)
Rank | Submission Information | Evaluation Dataset | |||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Submission Code |
Technical Report |
Official Rank |
AutoTrash (F1 score) |
AutoTrash (Recall) |
AutoTrash (Precision) |
BandSealer (F1 score) |
BandSealer (Recall) |
BandSealer (Precision) |
CoffeeGrinder (F1 score) |
CoffeeGrinder (Recall) |
CoffeeGrinder (Precision) |
HomeCamera (F1 score) |
HomeCamera (Recall) |
HomeCamera (Precision) |
Polisher (F1 score) |
Polisher (Recall) |
Polisher (Precision) |
ScrewFeeder (F1 score) |
ScrewFeeder (Recall) |
ScrewFeeder (Precision) |
ToyPet (F1 score) |
ToyPet (Recall) |
ToyPet (Precision) |
ToyRCCar (F1 score) |
ToyRCCar (Recall) |
ToyRCCar (Precision) |
|
DCASE2025_baseline_task2_MAHALA | DCASE2025baseline2025 | 58 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
DCASE2025_baseline_task2_MSE | DCASE2025baseline2025 | 77 | 65.40 | 92.47 | 50.59 | 63.04 | 78.20 | 52.80 | 58.31 | 66.67 | 51.81 | 67.34 | 100.00 | 50.76 | 64.73 | 86.07 | 51.87 | 66.67 | 100.00 | 50.00 | 57.31 | 68.42 | 49.30 | 32.21 | 22.70 | 55.41 | |
Zhou_XJU_task2_1 | ZhouXJU2025 | 100 | 31.84 | 21.38 | 62.29 | 57.06 | 54.10 | 60.36 | 26.08 | 18.08 | 46.81 | 52.20 | 45.71 | 60.84 | 53.88 | 52.80 | 55.00 | 62.00 | 62.00 | 62.00 | 52.02 | 45.00 | 61.64 | 49.37 | 48.63 | 50.14 | |
Zhou_XJU_task2_2 | ZhouXJU2025 | 94 | 32.75 | 21.43 | 69.44 | 57.30 | 55.52 | 59.19 | 31.47 | 21.38 | 59.57 | 49.53 | 41.75 | 60.87 | 52.27 | 50.37 | 54.31 | 60.93 | 60.98 | 60.88 | 51.59 | 44.75 | 60.91 | 50.27 | 49.23 | 51.36 | |
Zhou_XJU_task2_3 | ZhouXJU2025 | 96 | 35.96 | 24.44 | 68.09 | 52.62 | 48.86 | 57.00 | 27.14 | 18.11 | 54.11 | 54.70 | 49.26 | 61.50 | 53.70 | 51.93 | 55.61 | 58.92 | 58.98 | 58.86 | 49.26 | 42.25 | 59.08 | 52.99 | 52.98 | 53.00 | |
Zhou_XJU_task2_4 | ZhouXJU2025 | 93 | 28.65 | 18.15 | 68.06 | 54.38 | 51.43 | 57.69 | 34.63 | 24.37 | 59.82 | 58.48 | 54.10 | 63.64 | 51.58 | 49.81 | 53.48 | 58.70 | 58.85 | 58.55 | 49.70 | 43.93 | 57.21 | 54.00 | 54.00 | 54.00 | |
Cai_NCUT_task2_1 | CaiNCUT2025 | 45 | 79.90 | 80.78 | 79.05 | 25.39 | 16.88 | 51.23 | 39.39 | 30.21 | 56.59 | 29.83 | 21.27 | 49.89 | 38.43 | 28.89 | 57.40 | 69.89 | 67.94 | 71.95 | 62.37 | 60.34 | 64.55 | 27.27 | 17.14 | 66.67 | |
Cai_NCUT_task2_2 | CaiNCUT2025 | 83 | 68.36 | 96.91 | 52.81 | 60.27 | 68.62 | 53.74 | 61.10 | 71.79 | 53.17 | 63.15 | 76.15 | 53.95 | 64.01 | 83.58 | 51.86 | 67.12 | 98.99 | 50.77 | 56.52 | 68.42 | 48.15 | 26.72 | 17.06 | 61.57 | |
Cai_NCUT_task2_3 | CaiNCUT2025 | 42 | 68.43 | 63.55 | 74.11 | 50.77 | 47.08 | 55.09 | 35.00 | 24.44 | 61.65 | 55.29 | 54.84 | 55.75 | 53.30 | 48.07 | 59.80 | 75.28 | 73.42 | 77.24 | 52.10 | 42.93 | 66.26 | 54.58 | 54.84 | 54.32 | |
Saengthong_SCITOK_task2_1 | SaengthongSCITOK2025 | 84 | 70.42 | 82.29 | 61.54 | 45.23 | 35.04 | 63.79 | 51.04 | 43.75 | 61.24 | 51.28 | 45.94 | 58.03 | 58.89 | 51.33 | 69.06 | 66.65 | 97.96 | 50.51 | 62.85 | 75.34 | 53.91 | 45.12 | 38.96 | 53.59 | |
Saengthong_SCITOK_task2_2 | SaengthongSCITOK2025 | 2 | 80.32 | 76.68 | 84.32 | 42.25 | 29.76 | 72.81 | 47.48 | 38.18 | 62.78 | 28.11 | 19.09 | 53.30 | 56.48 | 46.47 | 71.98 | 69.92 | 97.96 | 54.36 | 64.37 | 74.08 | 56.91 | 42.21 | 35.10 | 52.94 | |
Saengthong_SCITOK_task2_3 | SaengthongSCITOK2025 | 56 | 69.82 | 96.99 | 54.54 | 64.03 | 80.89 | 52.98 | 59.06 | 73.42 | 49.40 | 44.84 | 37.50 | 55.76 | 66.67 | 100.00 | 50.00 | 69.44 | 100.00 | 53.19 | 62.46 | 60.42 | 64.64 | 60.51 | 71.94 | 52.22 | |
Saengthong_SCITOK_task2_4 | SaengthongSCITOK2025 | 80 | 65.00 | 78.20 | 55.62 | 52.11 | 44.21 | 63.44 | 57.14 | 61.11 | 53.66 | 59.14 | 61.11 | 57.29 | 66.81 | 69.09 | 64.69 | 67.61 | 83.81 | 56.66 | 58.99 | 66.22 | 53.19 | 38.49 | 28.89 | 57.65 | |
Zhang_DKU_task2_1 | ZhangDKU2025 | 88 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Zhang_DKU_task2_2 | ZhangDKU2025 | 70 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Zhang_DKU_task2_3 | ZhangDKU2025 | 92 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Zhang_DKU_task2_4 | ZhangDKU2025 | 63 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
WT_IACAS_task2_1 | WTIACAS2025 | 66 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
WT_IACAS_task2_2 | WTIACAS2025 | 44 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
WT_IACAS_task2_3 | WTIACAS2025 | 86 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
WT_IACAS_task2_4 | WTIACAS2025 | 76 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Zhou_XAUAT_task2_1 | ZhouXAUAT2025 | 24 | 82.05 | 84.71 | 79.56 | 54.81 | 55.36 | 54.27 | 55.75 | 62.60 | 50.24 | 57.69 | 60.00 | 55.56 | 65.82 | 78.00 | 56.93 | 63.10 | 75.95 | 53.96 | 62.51 | 73.78 | 54.22 | 56.57 | 68.44 | 48.20 | |
Zhou_XAUAT_task2_2 | ZhouXAUAT2025 | 43 | 81.84 | 87.27 | 77.05 | 56.67 | 58.58 | 54.87 | 56.66 | 64.48 | 50.52 | 61.09 | 65.94 | 56.90 | 66.12 | 80.00 | 56.34 | 61.25 | 72.99 | 52.76 | 61.93 | 74.88 | 52.79 | 55.19 | 63.01 | 49.09 | |
Zhou_XAUAT_task2_3 | ZhouXAUAT2025 | 95 | 74.46 | 92.47 | 62.32 | 61.75 | 73.42 | 53.29 | 60.16 | 74.00 | 50.68 | 58.84 | 67.20 | 52.34 | 62.64 | 79.61 | 51.63 | 55.58 | 68.87 | 46.59 | 62.45 | 78.22 | 51.97 | 62.14 | 79.61 | 50.95 | |
Zhou_XAUAT_task2_4 | ZhouXAUAT2025 | 50 | 84.01 | 87.59 | 80.71 | 62.00 | 76.54 | 52.10 | 68.29 | 78.99 | 60.15 | 57.30 | 58.98 | 55.72 | 67.06 | 88.45 | 54.00 | 61.80 | 72.00 | 54.14 | 62.48 | 80.78 | 50.94 | 59.43 | 77.43 | 48.22 | |
Zhong_USTC_task2_1 | ZhongUSTC2025 | 54 | 51.94 | 51.92 | 51.96 | 55.01 | 54.98 | 55.04 | 46.96 | 46.98 | 46.94 | 52.78 | 52.53 | 53.03 | 56.97 | 56.98 | 56.96 | 54.90 | 54.84 | 54.96 | 53.91 | 53.93 | 53.89 | 51.49 | 51.69 | 51.30 | |
Zhong_USTC_task2_2 | ZhongUSTC2025 | 51 | 51.94 | 51.92 | 51.96 | 55.01 | 54.98 | 55.04 | 46.96 | 46.98 | 46.94 | 52.78 | 52.53 | 53.03 | 56.97 | 56.98 | 56.96 | 54.90 | 54.84 | 54.96 | 53.91 | 53.93 | 53.89 | 51.49 | 51.69 | 51.30 | |
Zhong_USTC_task2_3 | ZhongUSTC2025 | 38 | 51.94 | 51.92 | 51.96 | 55.01 | 54.98 | 55.04 | 46.96 | 46.98 | 46.94 | 52.78 | 52.53 | 53.03 | 56.97 | 56.98 | 56.96 | 54.90 | 54.84 | 54.96 | 53.91 | 53.93 | 53.89 | 51.49 | 51.69 | 51.30 | |
Zhong_USTC_task2_4 | ZhongUSTC2025 | 30 | 51.94 | 51.92 | 51.96 | 55.01 | 54.98 | 55.04 | 46.96 | 46.98 | 46.94 | 52.78 | 52.53 | 53.03 | 56.97 | 56.98 | 56.96 | 54.90 | 54.84 | 54.96 | 53.91 | 53.93 | 53.89 | 51.49 | 51.69 | 51.30 | |
Vijayyan_SNUC_task2_1 | VijayyanSNUC2025 | 109 | 70.88 | 82.02 | 62.40 | 52.53 | 48.41 | 57.42 | 36.52 | 31.84 | 42.81 | 49.20 | 49.65 | 48.75 | 50.07 | 45.77 | 55.27 | 69.36 | 94.99 | 54.62 | 62.46 | 70.13 | 56.30 | 33.33 | 25.55 | 47.94 | |
CHUNG_KUCAU_task2_1 | CHUNGKUCAU2025 | 97 | 64.64 | 71.35 | 59.09 | 44.90 | 36.07 | 59.46 | 21.30 | 14.48 | 40.32 | 54.52 | 54.12 | 54.92 | 34.74 | 25.55 | 54.25 | 53.39 | 37.74 | 91.20 | 56.91 | 55.76 | 58.12 | 39.34 | 32.74 | 49.27 | |
CHUNG_KUCAU_task2_2 | CHUNGKUCAU2025 | 65 | 49.53 | 34.60 | 87.12 | 48.07 | 41.74 | 56.67 | 44.17 | 35.80 | 57.64 | 51.84 | 42.35 | 66.79 | 42.29 | 34.91 | 53.63 | 25.89 | 17.78 | 47.62 | 56.37 | 53.77 | 59.23 | 25.95 | 19.20 | 40.00 | |
CHUNG_KUCAU_task2_3 | CHUNGKUCAU2025 | 72 | 63.98 | 58.80 | 70.17 | 50.13 | 43.75 | 58.68 | 0.00 | 0.00 | 0.00 | 59.55 | 61.33 | 57.86 | 31.94 | 21.76 | 60.04 | 38.71 | 24.00 | 100.00 | 0.00 | 0.00 | 0.00 | 38.97 | 30.91 | 52.71 | |
CHUNG_KUCAU_task2_4 | CHUNGKUCAU2025 | 47 | 48.92 | 33.92 | 87.72 | 42.98 | 30.71 | 71.58 | 48.73 | 38.40 | 66.67 | 58.06 | 48.00 | 73.47 | 38.43 | 27.00 | 66.67 | 67.86 | 72.65 | 63.67 | 57.12 | 58.23 | 56.04 | 25.44 | 17.78 | 44.69 | |
Dung_CNTT1PTIT_task2_1 | DungCNTT1PTIT2025 | 112 | 64.40 | 94.99 | 48.72 | 46.78 | 42.79 | 51.60 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Zhang_NWPU_task2_1 | ZhangNWPU2025 | 103 | 27.51 | 17.62 | 62.71 | 41.93 | 38.97 | 45.37 | 51.12 | 47.67 | 55.11 | 44.11 | 40.98 | 47.75 | 43.95 | 40.78 | 47.66 | 47.96 | 44.44 | 52.08 | 48.34 | 44.98 | 52.25 | 42.98 | 39.02 | 47.82 | |
Zhang_NWPU_task2_2 | ZhangNWPU2025 | 67 | 58.76 | 49.35 | 72.58 | 46.85 | 45.91 | 47.83 | 53.06 | 52.00 | 54.17 | 49.94 | 48.82 | 51.11 | 52.87 | 51.69 | 54.11 | 51.42 | 50.04 | 52.88 | 52.01 | 50.98 | 53.08 | 51.02 | 50.00 | 52.08 | |
Zhang_NWPU_task2_3 | ZhangNWPU2025 | 75 | 53.76 | 42.93 | 71.88 | 45.91 | 44.98 | 46.87 | 52.96 | 51.92 | 54.04 | 48.96 | 47.92 | 50.04 | 53.00 | 51.92 | 54.13 | 52.66 | 51.31 | 54.10 | 51.02 | 50.00 | 52.08 | 48.98 | 47.92 | 50.09 | |
Zhang_NWPU_task2_4 | ZhangNWPU2025 | 82 | 51.56 | 40.34 | 71.43 | 47.42 | 46.00 | 48.94 | 53.49 | 51.92 | 55.15 | 48.41 | 46.98 | 49.93 | 53.33 | 51.69 | 55.08 | 52.28 | 50.51 | 54.19 | 49.48 | 48.00 | 51.06 | 47.32 | 45.65 | 49.11 | |
Chao_BUCT_task2_1 | ChaoBUCT2025 | 118 | 53.45 | 53.33 | 53.57 | 39.73 | 33.92 | 47.94 | 61.54 | 61.74 | 61.35 | 52.82 | 48.44 | 58.06 | 55.67 | 55.71 | 55.63 | 38.00 | 38.00 | 38.00 | 48.92 | 48.98 | 48.86 | 43.35 | 39.32 | 48.30 | |
Chao_BUCT_task2_2 | ChaoBUCT2025 | 114 | 66.67 | 100.00 | 50.00 | 31.14 | 22.06 | 52.91 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 48.83 | 48.72 | 48.94 | 67.62 | 89.82 | 54.22 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Chao_BUCT_task2_3 | ChaoBUCT2025 | 108 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Li_XJTLU_task2_1 | LiXJTLU2025 | 89 | 69.14 | 68.64 | 69.65 | 47.95 | 47.67 | 48.23 | 32.62 | 24.00 | 50.91 | 55.05 | 51.43 | 59.21 | 56.61 | 56.56 | 56.66 | 49.85 | 49.41 | 50.30 | 46.97 | 41.21 | 54.60 | 52.73 | 52.15 | 53.33 | |
Li_XJTLU_task2_2 | LiXJTLU2025 | 87 | 69.14 | 68.64 | 69.65 | 55.00 | 54.98 | 55.02 | 32.62 | 24.00 | 50.91 | 46.06 | 44.88 | 47.30 | 51.84 | 48.44 | 55.76 | 49.85 | 49.41 | 50.30 | 46.97 | 41.21 | 54.60 | 53.73 | 51.43 | 56.25 | |
Li_XJTLU_task2_3 | LiXJTLU2025 | 106 | 60.34 | 60.20 | 60.47 | 43.51 | 38.20 | 50.54 | 44.86 | 37.53 | 55.75 | 50.04 | 49.92 | 50.16 | 53.27 | 50.37 | 56.53 | 48.74 | 48.49 | 48.99 | 57.53 | 55.19 | 60.07 | 49.00 | 48.98 | 49.02 | |
Li_XJTLU_task2_4 | LiXJTLU2025 | 99 | 61.03 | 56.77 | 65.97 | 46.10 | 37.53 | 59.76 | 39.11 | 33.70 | 46.59 | 45.10 | 43.83 | 46.45 | 52.86 | 52.83 | 52.89 | 68.69 | 68.64 | 68.74 | 56.00 | 53.05 | 59.29 | 52.70 | 52.08 | 53.34 | |
Wang_ZJU_task2_1 | WangZJU2025 | 107 | 0.00 | 0.00 | 0.00 | 20.78 | 12.80 | 55.17 | 25.19 | 18.18 | 40.98 | 21.12 | 12.80 | 60.38 | 22.73 | 13.75 | 65.48 | 0.00 | 0.00 | 0.00 | 7.00 | 3.80 | 44.19 | 0.00 | 0.00 | 0.00 | |
Wang_ZJU_task2_2 | WangZJU2025 | 64 | 66.67 | 100.00 | 50.00 | 62.05 | 75.00 | 52.91 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Wang_ZJU_task2_3 | WangZJU2025 | 85 | 71.00 | 90.11 | 58.57 | 59.65 | 68.62 | 52.76 | 59.98 | 68.42 | 53.39 | 62.16 | 77.47 | 51.91 | 64.62 | 84.84 | 52.19 | 0.00 | 0.00 | 0.00 | 55.87 | 66.67 | 48.08 | 56.90 | 53.43 | 60.86 | |
Wang_ZJU_task2_4 | WangZJU2025 | 55 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Lin_IASP_task2_1 | LinIASP2025 | 105 | 78.39 | 76.88 | 79.96 | 46.36 | 40.63 | 53.99 | 45.36 | 42.79 | 48.27 | 44.97 | 40.78 | 50.12 | 28.03 | 19.83 | 47.79 | 41.71 | 37.89 | 46.39 | 41.27 | 33.22 | 54.46 | 53.78 | 55.93 | 51.79 | |
Lin_IASP_task2_2 | LinIASP2025 | 78 | 77.87 | 77.95 | 77.79 | 53.35 | 52.08 | 54.70 | 52.36 | 46.34 | 60.16 | 53.97 | 52.15 | 55.92 | 53.42 | 49.26 | 58.35 | 54.60 | 54.55 | 54.64 | 46.89 | 43.92 | 50.29 | 58.99 | 58.98 | 59.00 | |
Lin_IASP_task2_3 | LinIASP2025 | 74 | 75.58 | 77.97 | 73.33 | 40.14 | 34.69 | 47.62 | 52.51 | 52.94 | 52.08 | 48.31 | 45.04 | 52.10 | 58.32 | 54.62 | 62.56 | 55.32 | 49.92 | 62.03 | 35.24 | 25.26 | 58.25 | 61.45 | 62.86 | 60.11 | |
Lin_IASP_task2_4 | LinIASP2025 | 73 | 64.35 | 64.48 | 64.23 | 52.48 | 51.31 | 53.70 | 54.98 | 54.11 | 55.88 | 47.37 | 47.67 | 47.08 | 51.67 | 50.72 | 52.66 | 56.96 | 56.84 | 57.08 | 50.85 | 50.08 | 51.65 | 54.32 | 54.11 | 54.53 | |
Lobanov_ITMO_task2_1 | LobanovITMO2025 | 110 | 69.89 | 96.91 | 54.65 | 45.98 | 41.62 | 51.35 | 40.00 | 32.00 | 53.33 | 36.59 | 30.00 | 46.88 | 41.82 | 36.28 | 49.37 | 19.44 | 12.92 | 39.25 | 34.55 | 31.06 | 38.94 | 35.49 | 25.20 | 60.00 | |
Lobanov_ITMO_task2_2 | LobanovITMO2025 | 98 | 68.53 | 98.99 | 52.41 | 44.89 | 37.89 | 55.05 | 32.17 | 25.71 | 42.96 | 23.17 | 14.12 | 64.52 | 34.63 | 27.87 | 45.73 | 28.07 | 19.20 | 52.17 | 32.60 | 25.87 | 44.04 | 43.62 | 38.57 | 50.19 | |
Qian_nivic_task2_1 | Qiannivic2025 | 32 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Qian_nivic_task2_2 | Qiannivic2025 | 28 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Qian_nivic_task2_3 | Qiannivic2025 | 49 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Qian_nivic_task2_4 | Qiannivic2025 | 39 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Wang_MYPS_task2_1 | WangMYPS2025 | 16 | 74.16 | 71.35 | 77.19 | 57.68 | 57.72 | 57.64 | 57.92 | 56.95 | 58.93 | 47.09 | 46.98 | 47.20 | 58.00 | 58.00 | 58.00 | 87.06 | 86.71 | 87.42 | 56.48 | 55.19 | 57.83 | 48.94 | 48.49 | 49.40 | |
Wang_MYPS_task2_2 | WangMYPS2025 | 20 | 76.63 | 76.15 | 77.10 | 57.68 | 57.72 | 57.64 | 57.92 | 56.95 | 58.93 | 47.09 | 46.98 | 47.20 | 59.73 | 59.73 | 59.73 | 86.13 | 85.81 | 86.46 | 56.48 | 55.19 | 57.83 | 48.94 | 48.49 | 49.40 | |
Wang_MYPS_task2_3 | WangMYPS2025 | 1 | 73.43 | 71.89 | 75.04 | 59.46 | 59.40 | 59.52 | 52.00 | 51.47 | 52.54 | 59.97 | 59.93 | 60.01 | 61.98 | 61.74 | 62.22 | 83.02 | 82.99 | 83.05 | 59.02 | 58.33 | 59.73 | 44.00 | 43.64 | 44.36 | |
Wang_MYPS_task2_4 | WangMYPS2025 | 4 | 71.69 | 67.83 | 76.02 | 59.46 | 59.40 | 59.52 | 52.00 | 51.47 | 52.54 | 59.97 | 59.93 | 60.01 | 64.83 | 64.86 | 64.80 | 82.05 | 81.95 | 82.15 | 59.02 | 58.33 | 59.73 | 44.00 | 43.64 | 44.36 | |
Emon_HDK_task2_1 | EmonHDK2025 | 121 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 6.81 | 3.69 | 43.64 | 10.70 | 6.40 | 32.65 | 13.95 | 8.40 | 41.18 | 0.00 | 0.00 | 0.00 | 11.91 | 6.77 | 49.44 | |
Fu_CUMT_task2_1 | FuCUMT2025 | 52 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Fu_CUMT_task2_2 | FuCUMT2025 | 40 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Fu_CUMT_task2_3 | FuCUMT2025 | 41 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Fu_CUMT_task2_4 | FuCUMT2025 | 26 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Ding_HFUU_task2_1 | DingHFUU2025 | 71 | 68.97 | 100.00 | 52.63 | 63.16 | 79.52 | 52.38 | 60.18 | 79.52 | 48.41 | 64.05 | 92.73 | 48.92 | 62.57 | 83.58 | 50.00 | 0.00 | 0.00 | 0.00 | 62.40 | 87.64 | 48.45 | 0.00 | 0.00 | 0.00 | |
Ding_HFUU_task2_2 | DingHFUU2025 | 81 | 63.89 | 63.01 | 64.79 | 0.00 | 0.00 | 0.00 | 49.26 | 46.15 | 52.82 | 64.98 | 94.74 | 49.45 | 64.66 | 92.47 | 49.71 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 57.32 | 60.88 | 54.16 | |
Ding_HFUU_task2_3 | DingHFUU2025 | 91 | 41.85 | 32.98 | 57.25 | 60.06 | 70.68 | 52.22 | 57.46 | 64.86 | 51.57 | 64.09 | 80.96 | 53.03 | 61.41 | 78.22 | 50.55 | 64.98 | 90.00 | 50.85 | 56.18 | 66.67 | 48.54 | 29.34 | 20.00 | 55.05 | |
Ding_HFUU_task2_4 | DingHFUU2025 | 59 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Yang_NBU_task2_1 | YangNBU2025 | 3 | 51.92 | 51.92 | 51.92 | 47.61 | 47.25 | 47.97 | 51.85 | 51.69 | 52.01 | 44.05 | 43.91 | 44.19 | 53.01 | 52.98 | 53.04 | 49.92 | 49.92 | 49.92 | 42.00 | 42.00 | 42.00 | 52.89 | 52.83 | 52.95 | |
Yang_NBU_task2_2 | YangNBU2025 | 6 | 51.92 | 51.92 | 51.92 | 47.61 | 47.25 | 47.97 | 51.85 | 51.69 | 52.01 | 44.05 | 43.91 | 44.19 | 53.01 | 52.98 | 53.04 | 49.92 | 49.92 | 49.92 | 42.00 | 42.00 | 42.00 | 52.89 | 52.83 | 52.95 | |
Yang_NBU_task2_3 | YangNBU2025 | 5 | 51.92 | 51.92 | 51.92 | 47.61 | 47.25 | 47.97 | 51.85 | 51.69 | 52.01 | 44.05 | 43.91 | 44.19 | 53.01 | 52.98 | 53.04 | 49.92 | 49.92 | 49.92 | 42.00 | 42.00 | 42.00 | 52.89 | 52.83 | 52.95 | |
Yang_NBU_task2_4 | YangNBU2025 | 8 | 51.92 | 51.92 | 51.92 | 47.61 | 47.25 | 47.97 | 51.85 | 51.69 | 52.01 | 44.05 | 43.91 | 44.19 | 53.01 | 52.98 | 53.04 | 49.92 | 49.92 | 49.92 | 42.00 | 42.00 | 42.00 | 52.89 | 52.83 | 52.95 | |
Kret_CU_task2_1 | KretCU2025 | 115 | 0.00 | 0.00 | 0.00 | 6.79 | 3.69 | 42.11 | 41.83 | 36.98 | 48.15 | 57.10 | 61.11 | 53.58 | 24.62 | 15.65 | 57.69 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Zheng_SJTU-AITHU_task2_1 | ZhengSJTU-AITHU2025 | 13 | 81.01 | 80.89 | 81.13 | 63.95 | 63.75 | 64.15 | 46.54 | 45.55 | 47.57 | 59.00 | 58.98 | 59.02 | 56.03 | 55.71 | 56.36 | 61.94 | 61.94 | 61.94 | 56.07 | 54.88 | 57.31 | 45.93 | 43.92 | 48.13 | |
Zheng_SJTU-AITHU_task2_2 | ZhengSJTU-AITHU2025 | 11 | 85.81 | 85.26 | 86.38 | 64.07 | 63.75 | 64.39 | 48.81 | 47.69 | 49.98 | 56.05 | 55.93 | 56.17 | 57.37 | 56.90 | 57.85 | 61.96 | 61.94 | 61.98 | 56.81 | 55.52 | 58.16 | 45.88 | 42.67 | 49.61 | |
Zheng_SJTU-AITHU_task2_3 | ZhengSJTU-AITHU2025 | 15 | 85.81 | 85.26 | 86.38 | 66.06 | 65.45 | 66.67 | 47.27 | 46.08 | 48.53 | 56.00 | 56.00 | 56.00 | 56.02 | 55.58 | 56.47 | 60.89 | 60.59 | 61.19 | 54.26 | 52.50 | 56.15 | 45.09 | 39.00 | 53.42 | |
Zheng_SJTU-AITHU_task2_4 | ZhengSJTU-AITHU2025 | 12 | 85.81 | 85.26 | 86.38 | 64.07 | 63.75 | 64.39 | 48.81 | 47.69 | 49.98 | 56.05 | 55.93 | 56.17 | 56.11 | 55.58 | 56.65 | 62.99 | 62.98 | 63.00 | 56.81 | 55.52 | 58.16 | 45.52 | 41.63 | 50.20 | |
Zhao_CUMT_task2_1 | ZhaoCUMT2025 | 102 | 68.86 | 96.91 | 53.41 | 20.92 | 13.22 | 50.17 | 17.33 | 11.16 | 38.71 | 22.16 | 13.33 | 65.57 | 32.73 | 24.00 | 51.43 | 0.00 | 0.00 | 0.00 | 42.91 | 41.48 | 44.44 | 0.00 | 0.00 | 0.00 | |
Zhao_CUMT_task2_2 | ZhaoCUMT2025 | 101 | 68.86 | 96.91 | 53.41 | 20.92 | 13.22 | 50.17 | 17.33 | 11.16 | 38.71 | 22.16 | 13.33 | 65.57 | 32.73 | 24.00 | 51.43 | 0.00 | 0.00 | 0.00 | 42.91 | 41.48 | 44.44 | 0.00 | 0.00 | 0.00 | |
Zhao_CUMT_task2_3 | ZhaoCUMT2025 | 61 | 61.50 | 60.85 | 62.16 | 20.92 | 13.22 | 50.17 | 17.33 | 11.16 | 38.71 | 22.16 | 13.33 | 65.57 | 32.73 | 24.00 | 51.43 | 0.00 | 0.00 | 0.00 | 42.91 | 41.48 | 44.44 | 0.00 | 0.00 | 0.00 | |
Zhao_CUMT_task2_4 | ZhaoCUMT2025 | 62 | 61.50 | 60.85 | 62.16 | 20.92 | 13.22 | 50.17 | 17.33 | 11.16 | 38.71 | 22.16 | 13.33 | 65.57 | 32.73 | 24.00 | 51.43 | 0.00 | 0.00 | 0.00 | 42.91 | 41.48 | 44.44 | 0.00 | 0.00 | 0.00 | |
Ozeki_MELCO_task2_1 | OzekiMELCO2025 | 23 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Ozeki_MELCO_task2_2 | OzekiMELCO2025 | 60 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Ozeki_MELCO_task2_3 | OzekiMELCO2025 | 69 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Ozeki_MELCO_task2_4 | OzekiMELCO2025 | 79 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Huang_XJU_task2_1 | HuangXJU2025 | 25 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Huang_XJU_task2_2 | HuangXJU2025 | 27 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 70.59 | 76.80 | 65.31 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Huang_XJU_task2_3 | HuangXJU2025 | 35 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Huang_XJU_task2_4 | HuangXJU2025 | 37 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 23.79 | 14.44 | 67.36 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Fujimura_NU_task2_1 | FujimuraNU2025 | 7 | 66.89 | 100.00 | 50.25 | 68.19 | 75.95 | 61.86 | 0.00 | 0.00 | 0.00 | 48.60 | 38.77 | 65.12 | 5.19 | 2.67 | 100.00 | 66.67 | 100.00 | 50.00 | 62.57 | 70.74 | 56.09 | 66.67 | 100.00 | 50.00 | |
Fujimura_NU_task2_2 | FujimuraNU2025 | 21 | 66.67 | 100.00 | 50.00 | 68.65 | 92.90 | 54.44 | 0.00 | 0.00 | 0.00 | 63.88 | 67.83 | 60.37 | 63.32 | 87.27 | 49.69 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Fujimura_NU_task2_3 | FujimuraNU2025 | 14 | 67.34 | 100.00 | 50.76 | 68.09 | 96.00 | 52.75 | 13.41 | 7.47 | 65.88 | 65.60 | 79.80 | 55.69 | 58.78 | 61.77 | 56.07 | 66.67 | 100.00 | 50.00 | 65.45 | 86.36 | 52.69 | 66.67 | 100.00 | 50.00 | |
Fujimura_NU_task2_4 | FujimuraNU2025 | 9 | 67.34 | 100.00 | 50.76 | 67.62 | 92.99 | 53.13 | 13.57 | 7.53 | 68.82 | 66.89 | 87.95 | 53.97 | 46.46 | 33.95 | 73.57 | 66.67 | 100.00 | 50.00 | 62.07 | 71.27 | 54.98 | 66.67 | 100.00 | 50.00 | |
Jiang_THUEE_task2_1 | JiangTHUEE2025 | 19 | 86.86 | 86.44 | 87.28 | 64.10 | 63.44 | 64.77 | 48.81 | 47.69 | 49.98 | 56.07 | 55.93 | 56.21 | 54.39 | 54.11 | 54.67 | 62.95 | 62.86 | 63.04 | 54.40 | 52.50 | 56.45 | 43.97 | 38.48 | 51.28 | |
Jiang_THUEE_task2_2 | JiangTHUEE2025 | 10 | 88.81 | 88.09 | 89.54 | 64.15 | 63.75 | 64.56 | 50.81 | 49.81 | 51.85 | 55.02 | 54.98 | 55.06 | 57.53 | 56.90 | 58.18 | 65.72 | 65.76 | 65.68 | 58.14 | 56.95 | 59.38 | 46.35 | 43.92 | 49.07 | |
Jiang_THUEE_task2_3 | JiangTHUEE2025 | 18 | 86.61 | 86.07 | 87.15 | 63.21 | 62.60 | 63.82 | 50.27 | 49.23 | 51.36 | 56.95 | 56.98 | 56.92 | 56.63 | 56.14 | 57.12 | 64.96 | 64.98 | 64.94 | 55.82 | 54.04 | 57.72 | 43.51 | 38.20 | 50.54 | |
Jiang_THUEE_task2_4 | JiangTHUEE2025 | 17 | 88.63 | 87.64 | 89.63 | 63.13 | 62.22 | 64.07 | 48.93 | 47.69 | 50.25 | 56.95 | 56.98 | 56.92 | 55.49 | 54.86 | 56.14 | 62.00 | 62.00 | 62.00 | 54.96 | 53.43 | 56.58 | 46.26 | 42.00 | 51.47 | |
Bian_TGU_task2_1 | BianTGU2025 | 119 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 9.04 | 5.33 | 29.63 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 15.62 | 10.00 | 35.71 | |
Bian_TGU_task2_2 | BianTGU2025 | 116 | 13.26 | 8.00 | 38.71 | 33.03 | 24.30 | 51.56 | 18.15 | 10.91 | 54.05 | 14.68 | 8.73 | 46.15 | 14.04 | 8.00 | 57.14 | 12.60 | 6.86 | 77.42 | 6.15 | 3.33 | 40.00 | 14.65 | 8.89 | 41.67 | |
Bian_TGU_task2_3 | BianTGU2025 | 104 | 16.27 | 9.60 | 53.33 | 24.95 | 15.75 | 60.00 | 14.76 | 8.40 | 60.87 | 31.33 | 22.61 | 50.98 | 0.00 | 0.00 | 0.00 | 25.51 | 15.75 | 67.02 | 20.33 | 12.31 | 58.39 | 0.00 | 0.00 | 0.00 | |
Bian_TGU_task2_4 | BianTGU2025 | 120 | 17.10 | 10.18 | 53.33 | 0.00 | 0.00 | 0.00 | 17.69 | 11.73 | 35.92 | 36.35 | 28.97 | 48.78 | 0.00 | 0.00 | 0.00 | 17.14 | 10.91 | 40.00 | 0.00 | 0.00 | 0.00 | 16.67 | 10.67 | 38.10 | |
Sera_TMU_task2_1 | SeraTMU2025 | 57 | 0.00 | 0.00 | 0.00 | 7.17 | 3.75 | 81.08 | 0.00 | 0.00 | 0.00 | 20.00 | 12.00 | 60.00 | 7.01 | 3.69 | 68.57 | 32.79 | 20.00 | 90.91 | 0.00 | 0.00 | 0.00 | 18.43 | 10.91 | 59.41 | |
Kim_DAU_task2_1 | KimDAU2025 | 113 | 38.89 | 37.33 | 40.58 | 15.65 | 9.00 | 60.00 | 60.80 | 66.34 | 56.11 | 60.16 | 70.99 | 52.20 | 59.12 | 80.99 | 46.55 | 58.25 | 74.35 | 47.89 | 56.44 | 61.94 | 51.84 | 27.04 | 19.20 | 45.71 | |
Kim_DAU_task2_2 | KimDAU2025 | 111 | 48.79 | 45.96 | 52.00 | 16.34 | 9.75 | 50.32 | 54.26 | 62.40 | 48.00 | 58.03 | 73.42 | 47.97 | 55.43 | 60.98 | 50.81 | 19.29 | 12.92 | 38.01 | 28.39 | 19.80 | 50.13 | 25.63 | 18.20 | 43.33 | |
Wang_UniS_task2_1 | WangUniS2025 | 34 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Wang_UniS_task2_2 | WangUniS2025 | 46 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Wang_UniS_task2_3 | WangUniS2025 | 53 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Wang_UniS_task2_4 | WangUniS2025 | 90 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | 66.67 | 100.00 | 50.00 | |
Guan_HEU_task2_1 | GuanHEU2025 | 22 | 60.06 | 54.19 | 67.36 | 29.70 | 19.35 | 63.83 | 40.13 | 30.41 | 58.96 | 27.05 | 18.67 | 49.12 | 33.04 | 22.40 | 62.92 | 63.91 | 47.35 | 98.31 | 40.52 | 28.10 | 72.64 | 32.35 | 21.82 | 62.50 | |
Guan_HEU_task2_2 | GuanHEU2025 | 48 | 66.67 | 76.72 | 58.94 | 29.70 | 19.35 | 63.83 | 42.75 | 33.22 | 59.94 | 25.81 | 17.45 | 49.48 | 33.27 | 22.56 | 63.34 | 69.12 | 54.04 | 95.89 | 31.52 | 19.50 | 82.11 | 31.05 | 20.95 | 59.95 | |
Guan_HEU_task2_3 | GuanHEU2025 | 117 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 6.43 | 3.33 | 90.91 | 0.00 | 0.00 | 0.00 | 10.75 | 5.71 | 90.91 | 6.94 | 3.75 | 46.88 | 8.92 | 4.80 | 63.16 | |
Guan_HEU_task2_4 | GuanHEU2025 | 68 | 63.95 | 61.61 | 66.47 | 7.21 | 3.79 | 75.00 | 23.97 | 14.75 | 64.05 | 30.03 | 20.36 | 57.14 | 35.65 | 25.60 | 58.72 | 63.05 | 47.25 | 94.74 | 43.48 | 31.84 | 68.54 | 14.51 | 8.40 | 53.16 | |
Kim_AISTAT_task2_1 | KimAISTAT2025 | 31 | 79.25 | 84.00 | 75.00 | 26.93 | 17.73 | 56.03 | 23.74 | 14.72 | 61.33 | 65.74 | 60.59 | 71.85 | 34.86 | 27.47 | 47.67 | 66.89 | 100.00 | 50.25 | 68.47 | 97.96 | 52.63 | 52.76 | 56.84 | 49.23 | |
Kim_AISTAT_task2_2 | KimAISTAT2025 | 33 | 79.37 | 80.99 | 77.82 | 26.89 | 17.73 | 55.63 | 23.62 | 14.67 | 60.69 | 65.20 | 59.40 | 72.26 | 35.20 | 27.47 | 48.97 | 66.89 | 100.00 | 50.25 | 68.71 | 97.96 | 52.91 | 52.42 | 55.93 | 49.32 | |
Kim_AISTAT_task2_3 | KimAISTAT2025 | 36 | 82.21 | 82.99 | 81.44 | 30.59 | 20.80 | 57.78 | 23.68 | 14.72 | 60.53 | 65.66 | 61.42 | 70.52 | 37.08 | 29.74 | 49.24 | 66.89 | 100.00 | 50.25 | 68.53 | 96.99 | 52.98 | 53.52 | 56.84 | 50.56 | |
Kim_AISTAT_task2_4 | KimAISTAT2025 | 29 | 79.88 | 85.95 | 74.61 | 37.03 | 26.55 | 61.18 | 23.80 | 14.75 | 61.64 | 66.81 | 62.60 | 71.63 | 37.38 | 30.00 | 49.59 | 66.89 | 100.00 | 50.25 | 68.24 | 97.96 | 52.36 | 53.17 | 58.58 | 48.68 |
Domain-wise performance
Rank | Submission Information | Ranking | Eveluation Dataset in Source Domain | Eveluation Dataset in Target Domain | ||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Submission Code |
Technical Report |
Official Rank |
Official Score |
Harmonic mean (AUC, source) |
AutoTrash (AUC, source) |
AutoTrash (pAUC, source) |
BandSealer (AUC, source) |
BandSealer (pAUC, source) |
CoffeeGrinder (AUC, source) |
CoffeeGrinder (pAUC, source) |
HomeCamera (AUC, source) |
HomeCamera (pAUC, source) |
Polisher (AUC, source) |
Polisher (pAUC, source) |
ScrewFeeder (AUC, source) |
ScrewFeeder (pAUC, source) |
ToyPet (AUC, source) |
ToyPet (pAUC, source) |
ToyRCCar (AUC, source) |
ToyRCCar (pAUC, source) |
Harmonic mean (AUC, target) |
AutoTrash (AUC, target) |
AutoTrash (pAUC, target) |
BandSealer (AUC, target) |
BandSealer (pAUC, target) |
CoffeeGrinder (AUC, target) |
CoffeeGrinder (pAUC, target) |
HomeCamera (AUC, target) |
HomeCamera (pAUC, target) |
Polisher (AUC, target) |
Polisher (pAUC, target) |
ScrewFeeder (AUC, target) |
ScrewFeeder (pAUC, target) |
ToyPet (AUC, target) |
ToyPet (pAUC, target) |
ToyRCCar (AUC, target) |
ToyRCCar (pAUC, target) |
|
DCASE2025_baseline_task2_MAHALA | DCASE2025baseline2025 | 58 | 56.506 | 71.99 | 77.26 | 54.16 | 76.38 | 49.11 | 74.98 | 51.42 | 86.16 | 51.84 | 70.42 | 53.79 | 72.90 | 70.05 | 69.82 | 56.84 | 55.86 | 54.00 | 47.88 | 52.60 | 54.16 | 32.68 | 49.11 | 40.42 | 51.42 | 42.64 | 51.84 | 52.78 | 53.79 | 79.04 | 70.05 | 50.90 | 56.84 | 55.48 | 54.00 | |
DCASE2025_baseline_task2_MSE | DCASE2025baseline2025 | 77 | 54.428 | 68.80 | 81.02 | 54.21 | 71.98 | 52.05 | 73.04 | 53.42 | 81.40 | 52.84 | 66.86 | 52.32 | 64.86 | 62.11 | 67.70 | 55.00 | 52.84 | 55.53 | 44.95 | 34.36 | 54.21 | 39.56 | 52.05 | 44.36 | 53.42 | 49.76 | 52.84 | 44.30 | 52.32 | 65.92 | 62.11 | 36.70 | 55.00 | 62.72 | 55.53 | |
Zhou_XJU_task2_1 | ZhouXJU2025 | 100 | 52.417 | 67.81 | 85.56 | 49.53 | 68.76 | 50.89 | 70.26 | 50.16 | 77.56 | 52.84 | 64.56 | 51.42 | 61.14 | 59.11 | 74.90 | 54.32 | 51.50 | 53.32 | 42.63 | 31.94 | 49.53 | 45.18 | 50.89 | 31.00 | 50.16 | 35.62 | 52.84 | 47.08 | 51.42 | 73.64 | 59.11 | 44.90 | 54.32 | 59.54 | 53.32 | |
Zhou_XJU_task2_2 | ZhouXJU2025 | 94 | 52.848 | 67.52 | 86.70 | 49.53 | 68.78 | 51.89 | 72.82 | 50.68 | 77.60 | 52.95 | 64.58 | 51.47 | 57.92 | 57.37 | 75.84 | 53.79 | 50.36 | 53.42 | 43.62 | 30.74 | 49.53 | 49.60 | 51.89 | 34.76 | 50.68 | 35.70 | 52.95 | 46.24 | 51.47 | 72.80 | 57.37 | 44.72 | 53.79 | 61.70 | 53.42 | |
Zhou_XJU_task2_3 | ZhouXJU2025 | 96 | 52.631 | 68.73 | 86.58 | 51.05 | 69.86 | 50.47 | 71.64 | 51.26 | 79.10 | 54.11 | 64.88 | 51.05 | 57.70 | 59.74 | 74.34 | 53.58 | 56.36 | 53.74 | 42.42 | 30.46 | 51.05 | 44.52 | 50.47 | 31.20 | 51.26 | 39.80 | 54.11 | 46.00 | 51.05 | 72.50 | 59.74 | 43.64 | 53.58 | 56.52 | 53.74 | |
Zhou_XJU_task2_4 | ZhouXJU2025 | 93 | 52.907 | 68.55 | 87.88 | 51.11 | 71.04 | 52.32 | 73.38 | 52.16 | 80.80 | 53.74 | 64.60 | 50.58 | 57.92 | 57.11 | 75.16 | 53.42 | 52.10 | 52.00 | 43.19 | 29.30 | 51.11 | 46.58 | 52.32 | 33.90 | 52.16 | 42.48 | 53.74 | 45.54 | 50.58 | 71.50 | 57.11 | 43.08 | 53.42 | 57.38 | 52.00 | |
Cai_NCUT_task2_1 | CaiNCUT2025 | 45 | 57.344 | 74.44 | 92.46 | 66.26 | 66.24 | 56.26 | 76.52 | 52.47 | 69.62 | 50.05 | 74.00 | 50.21 | 80.28 | 63.84 | 83.06 | 58.21 | 62.04 | 53.05 | 47.75 | 78.86 | 66.26 | 47.74 | 56.26 | 31.42 | 52.47 | 39.12 | 50.05 | 45.96 | 50.21 | 79.28 | 63.84 | 46.02 | 58.21 | 48.96 | 53.05 | |
Cai_NCUT_task2_2 | CaiNCUT2025 | 83 | 54.058 | 67.83 | 85.12 | 53.68 | 70.72 | 52.63 | 73.38 | 52.79 | 74.32 | 53.42 | 67.20 | 51.11 | 63.08 | 58.42 | 66.90 | 54.74 | 51.86 | 56.32 | 44.94 | 35.66 | 53.68 | 42.76 | 52.63 | 43.70 | 52.79 | 43.54 | 53.42 | 45.26 | 51.11 | 64.04 | 58.42 | 36.18 | 54.74 | 64.92 | 56.32 | |
Cai_NCUT_task2_3 | CaiNCUT2025 | 42 | 57.559 | 74.16 | 92.43 | 61.29 | 69.14 | 54.89 | 75.59 | 52.63 | 68.36 | 50.26 | 75.29 | 51.24 | 81.64 | 67.08 | 81.55 | 58.13 | 59.03 | 52.50 | 48.49 | 64.44 | 61.29 | 49.83 | 54.89 | 32.89 | 52.63 | 37.61 | 50.26 | 44.03 | 51.24 | 85.24 | 67.08 | 47.01 | 58.13 | 59.90 | 52.50 | |
Saengthong_SCITOK_task2_1 | SaengthongSCITOK2025 | 84 | 53.815 | 79.57 | 91.64 | 54.68 | 76.98 | 54.21 | 84.64 | 50.89 | 78.66 | 50.26 | 84.50 | 53.00 | 78.02 | 67.32 | 82.14 | 58.00 | 65.44 | 52.74 | 40.15 | 41.26 | 54.68 | 38.58 | 54.21 | 31.14 | 50.89 | 25.32 | 50.26 | 49.48 | 53.00 | 79.60 | 67.32 | 44.00 | 58.00 | 45.52 | 52.74 | |
Saengthong_SCITOK_task2_2 | SaengthongSCITOK2025 | 2 | 61.569 | 69.52 | 87.56 | 71.05 | 63.32 | 57.37 | 78.36 | 52.00 | 47.82 | 52.32 | 78.92 | 56.68 | 79.94 | 73.47 | 79.32 | 57.84 | 61.00 | 51.63 | 58.40 | 84.62 | 71.05 | 61.46 | 57.37 | 40.30 | 52.00 | 57.88 | 52.32 | 58.82 | 56.68 | 89.08 | 73.47 | 58.16 | 57.84 | 46.78 | 51.63 | |
Saengthong_SCITOK_task2_3 | SaengthongSCITOK2025 | 56 | 56.636 | 72.99 | 82.60 | 58.11 | 70.24 | 55.74 | 74.92 | 50.42 | 65.32 | 50.21 | 77.08 | 49.16 | 89.16 | 81.63 | 75.44 | 57.68 | 58.30 | 52.89 | 46.94 | 65.58 | 58.11 | 50.30 | 55.74 | 32.30 | 50.42 | 38.10 | 50.21 | 29.52 | 49.16 | 93.46 | 81.63 | 59.58 | 57.68 | 59.78 | 52.89 | |
Saengthong_SCITOK_task2_4 | SaengthongSCITOK2025 | 80 | 54.326 | 75.62 | 80.68 | 50.89 | 75.36 | 52.79 | 78.70 | 50.42 | 81.50 | 50.05 | 80.34 | 56.00 | 71.68 | 61.89 | 76.52 | 59.16 | 63.74 | 52.32 | 42.66 | 38.18 | 50.89 | 42.66 | 52.79 | 32.32 | 50.42 | 31.54 | 50.05 | 55.76 | 56.00 | 72.98 | 61.89 | 45.98 | 59.16 | 45.70 | 52.32 | |
Zhang_DKU_task2_1 | ZhangDKU2025 | 88 | 53.462 | 47.34 | 58.78 | 60.11 | 61.70 | 54.58 | 53.74 | 51.84 | 29.04 | 52.05 | 51.54 | 51.05 | 60.24 | 54.05 | 49.10 | 52.11 | 37.94 | 50.84 | 61.77 | 80.68 | 60.11 | 44.74 | 54.58 | 63.94 | 51.84 | 70.22 | 52.05 | 55.22 | 51.05 | 68.56 | 54.05 | 57.86 | 52.11 | 67.04 | 50.84 | |
Zhang_DKU_task2_2 | ZhangDKU2025 | 70 | 55.184 | 53.60 | 78.42 | 67.21 | 38.04 | 51.00 | 52.84 | 50.58 | 56.68 | 52.00 | 54.58 | 53.58 | 53.04 | 54.00 | 53.54 | 53.11 | 56.20 | 50.21 | 58.72 | 90.00 | 67.21 | 67.36 | 51.00 | 61.08 | 50.58 | 39.62 | 52.00 | 59.26 | 53.58 | 68.96 | 54.00 | 55.64 | 53.11 | 51.76 | 50.21 | |
Zhang_DKU_task2_3 | ZhangDKU2025 | 92 | 52.980 | 52.83 | 68.74 | 57.21 | 54.80 | 52.11 | 35.16 | 48.84 | 42.88 | 54.63 | 53.86 | 56.53 | 57.22 | 56.47 | 59.96 | 50.05 | 70.70 | 49.53 | 53.13 | 84.82 | 57.21 | 57.12 | 52.11 | 51.72 | 48.84 | 64.72 | 54.63 | 65.78 | 56.53 | 67.18 | 56.47 | 46.20 | 50.05 | 28.78 | 49.53 | |
Zhang_DKU_task2_4 | ZhangDKU2025 | 63 | 55.762 | 53.01 | 80.08 | 65.21 | 50.46 | 51.84 | 52.34 | 51.74 | 35.96 | 55.32 | 53.50 | 54.21 | 62.32 | 56.42 | 56.60 | 52.26 | 51.82 | 50.79 | 60.40 | 86.06 | 65.21 | 64.16 | 51.84 | 58.90 | 51.74 | 62.90 | 55.32 | 64.60 | 54.21 | 75.28 | 56.42 | 48.54 | 52.26 | 43.46 | 50.79 | |
WT_IACAS_task2_1 | WTIACAS2025 | 66 | 55.534 | 79.46 | 95.30 | 67.26 | 84.20 | 58.58 | 84.06 | 53.11 | 87.38 | 52.95 | 84.04 | 57.16 | 55.74 | 56.53 | 86.16 | 53.95 | 73.92 | 50.84 | 42.45 | 68.40 | 67.26 | 52.54 | 58.58 | 37.42 | 53.11 | 35.90 | 52.95 | 42.36 | 57.16 | 62.80 | 56.53 | 40.82 | 53.95 | 27.64 | 50.84 | |
WT_IACAS_task2_2 | WTIACAS2025 | 44 | 57.441 | 78.79 | 93.48 | 72.74 | 75.68 | 61.37 | 84.28 | 51.63 | 84.64 | 52.89 | 84.82 | 56.63 | 62.32 | 62.84 | 85.80 | 55.00 | 69.18 | 51.21 | 45.28 | 81.84 | 72.74 | 59.86 | 61.37 | 35.66 | 51.63 | 35.84 | 52.89 | 43.82 | 56.63 | 71.24 | 62.84 | 45.14 | 55.00 | 30.48 | 51.21 | |
WT_IACAS_task2_3 | WTIACAS2025 | 86 | 53.521 | 78.33 | 96.82 | 64.84 | 86.30 | 57.74 | 84.90 | 51.95 | 87.08 | 52.79 | 83.28 | 55.21 | 49.72 | 53.26 | 86.44 | 54.63 | 75.36 | 50.89 | 39.90 | 54.72 | 64.84 | 46.68 | 57.74 | 35.54 | 51.95 | 35.54 | 52.79 | 38.54 | 55.21 | 55.18 | 53.26 | 39.10 | 54.63 | 28.66 | 50.89 | |
WT_IACAS_task2_4 | WTIACAS2025 | 76 | 54.440 | 77.45 | 95.96 | 66.32 | 83.84 | 58.89 | 84.90 | 51.63 | 85.84 | 53.16 | 83.66 | 54.26 | 49.64 | 55.53 | 85.92 | 56.42 | 72.60 | 52.32 | 41.23 | 58.56 | 66.32 | 47.36 | 58.89 | 34.12 | 51.63 | 35.38 | 53.16 | 38.78 | 54.26 | 57.96 | 55.53 | 42.52 | 56.42 | 31.52 | 52.32 | |
Zhou_XAUAT_task2_1 | ZhouXAUAT2025 | 24 | 58.188 | 56.49 | 89.52 | 79.16 | 59.14 | 50.63 | 55.90 | 51.84 | 65.24 | 55.32 | 62.22 | 54.05 | 38.82 | 51.42 | 60.22 | 53.58 | 45.68 | 54.79 | 63.38 | 91.66 | 79.16 | 52.50 | 50.63 | 54.40 | 51.84 | 59.12 | 55.32 | 63.26 | 54.05 | 81.14 | 51.42 | 63.58 | 53.58 | 58.48 | 54.79 | |
Zhou_XAUAT_task2_2 | ZhouXAUAT2025 | 43 | 57.510 | 54.30 | 90.44 | 79.68 | 55.88 | 50.21 | 58.24 | 52.89 | 66.26 | 54.63 | 55.86 | 53.63 | 36.82 | 51.21 | 59.70 | 54.63 | 41.14 | 53.63 | 63.90 | 90.62 | 79.68 | 50.74 | 50.21 | 55.52 | 52.89 | 61.10 | 54.63 | 66.50 | 53.63 | 74.66 | 51.21 | 57.10 | 54.63 | 70.82 | 53.63 | |
Zhou_XAUAT_task2_3 | ZhouXAUAT2025 | 95 | 52.663 | 53.92 | 96.48 | 77.00 | 54.48 | 50.74 | 68.84 | 49.42 | 72.14 | 51.16 | 55.88 | 53.95 | 27.00 | 48.53 | 55.28 | 52.47 | 54.82 | 53.79 | 50.61 | 76.60 | 77.00 | 58.98 | 50.74 | 27.94 | 49.42 | 37.38 | 51.16 | 57.84 | 53.95 | 63.64 | 48.53 | 61.14 | 52.47 | 61.84 | 53.79 | |
Zhou_XAUAT_task2_4 | ZhouXAUAT2025 | 50 | 57.092 | 55.87 | 91.62 | 81.63 | 56.88 | 51.16 | 84.06 | 49.84 | 64.42 | 56.05 | 61.10 | 53.89 | 37.20 | 51.05 | 53.34 | 51.74 | 39.56 | 53.47 | 60.92 | 91.58 | 81.63 | 58.90 | 51.16 | 44.18 | 49.84 | 56.32 | 56.05 | 67.66 | 53.89 | 76.16 | 51.05 | 54.34 | 51.74 | 59.84 | 53.47 | |
Zhong_USTC_task2_1 | ZhongUSTC2025 | 54 | 56.896 | 55.10 | 66.48 | 75.05 | 63.82 | 55.21 | 51.70 | 50.26 | 43.74 | 52.05 | 60.14 | 57.42 | 66.08 | 58.95 | 58.96 | 54.74 | 42.44 | 49.32 | 60.08 | 94.12 | 75.05 | 55.54 | 55.21 | 48.34 | 50.26 | 56.82 | 52.05 | 74.98 | 57.42 | 85.36 | 58.95 | 60.02 | 54.74 | 40.78 | 49.32 | |
Zhong_USTC_task2_2 | ZhongUSTC2025 | 51 | 57.068 | 56.37 | 75.79 | 56.10 | 63.82 | 55.21 | 51.70 | 50.26 | 43.74 | 52.05 | 64.52 | 62.16 | 67.48 | 63.53 | 58.96 | 54.74 | 42.44 | 49.32 | 60.05 | 87.73 | 56.10 | 55.54 | 55.21 | 48.34 | 50.26 | 56.82 | 52.05 | 77.80 | 62.16 | 87.02 | 63.53 | 60.02 | 54.74 | 40.78 | 49.32 | |
Zhong_USTC_task2_3 | ZhongUSTC2025 | 38 | 57.698 | 54.18 | 66.48 | 75.05 | 49.26 | 55.21 | 58.26 | 55.68 | 43.74 | 52.05 | 60.14 | 57.42 | 66.08 | 58.95 | 58.96 | 54.74 | 42.44 | 49.32 | 63.10 | 94.12 | 75.05 | 64.76 | 55.21 | 59.26 | 55.68 | 56.82 | 52.05 | 74.98 | 57.42 | 85.36 | 58.95 | 60.02 | 54.74 | 40.78 | 49.32 | |
Zhong_USTC_task2_4 | ZhongUSTC2025 | 30 | 57.875 | 55.42 | 75.79 | 56.10 | 49.26 | 55.21 | 58.26 | 55.68 | 43.74 | 52.05 | 64.52 | 62.16 | 67.48 | 63.53 | 58.96 | 54.74 | 42.44 | 49.32 | 63.07 | 87.73 | 56.10 | 64.76 | 55.21 | 59.26 | 55.68 | 56.82 | 52.05 | 77.80 | 62.16 | 87.02 | 63.53 | 60.02 | 54.74 | 40.78 | 49.32 | |
Vijayyan_SNUC_task2_1 | VijayyanSNUC2025 | 109 | 49.905 | 70.53 | 88.26 | 56.74 | 75.18 | 48.32 | 60.18 | 50.53 | 70.76 | 47.37 | 68.28 | 51.32 | 80.88 | 63.47 | 75.66 | 52.89 | 56.14 | 49.89 | 37.37 | 50.68 | 56.74 | 28.34 | 48.32 | 36.84 | 50.53 | 22.70 | 47.37 | 43.58 | 51.32 | 65.94 | 63.47 | 38.58 | 52.89 | 41.96 | 49.89 | |
CHUNG_KUCAU_task2_1 | CHUNGKUCAU2025 | 97 | 52.535 | 72.92 | 78.18 | 53.84 | 75.34 | 50.68 | 64.13 | 48.32 | 77.84 | 50.63 | 73.38 | 52.26 | 73.88 | 65.63 | 78.50 | 51.16 | 65.32 | 48.74 | 41.25 | 60.92 | 53.84 | 31.78 | 50.68 | 34.22 | 48.32 | 36.22 | 50.63 | 44.88 | 52.26 | 77.56 | 65.63 | 38.18 | 51.16 | 35.90 | 48.74 | |
CHUNG_KUCAU_task2_2 | CHUNGKUCAU2025 | 65 | 55.554 | 67.99 | 79.82 | 66.42 | 70.60 | 53.63 | 74.68 | 55.84 | 84.38 | 54.26 | 70.06 | 51.63 | 52.02 | 51.37 | 78.94 | 50.16 | 51.12 | 48.84 | 48.46 | 75.82 | 66.42 | 45.90 | 53.63 | 43.08 | 55.84 | 42.98 | 54.26 | 44.24 | 51.63 | 65.48 | 51.37 | 42.76 | 50.16 | 44.74 | 48.84 | |
CHUNG_KUCAU_task2_3 | CHUNGKUCAU2025 | 72 | 54.778 | 74.22 | 93.80 | 56.95 | 76.84 | 55.16 | 71.09 | 49.53 | 77.42 | 55.68 | 67.12 | 53.68 | 73.97 | 68.61 | 78.60 | 51.47 | 62.56 | 51.32 | 43.39 | 49.36 | 56.95 | 36.34 | 55.16 | 32.96 | 49.53 | 45.62 | 55.68 | 47.96 | 53.68 | 80.50 | 68.61 | 38.20 | 51.47 | 40.16 | 51.32 | |
CHUNG_KUCAU_task2_4 | CHUNGKUCAU2025 | 47 | 57.227 | 71.09 | 86.48 | 67.68 | 70.38 | 56.95 | 78.22 | 51.16 | 83.16 | 57.84 | 70.54 | 52.37 | 76.04 | 67.32 | 73.38 | 47.58 | 47.64 | 51.37 | 49.01 | 70.18 | 67.68 | 64.54 | 56.95 | 35.20 | 51.16 | 51.42 | 57.84 | 46.68 | 52.37 | 72.50 | 67.32 | 34.84 | 47.58 | 46.02 | 51.37 | |
Dung_CNTT1PTIT_task2_1 | DungCNTT1PTIT2025 | 112 | 48.719 | 48.97 | 44.60 | 54.58 | 48.74 | 48.68 | 59.92 | 51.74 | 53.10 | 51.05 | 52.58 | 50.58 | 40.46 | 49.11 | 43.32 | 48.68 | 55.26 | 52.47 | 46.58 | 34.20 | 54.58 | 51.46 | 48.68 | 49.82 | 51.74 | 50.02 | 51.05 | 50.70 | 50.58 | 46.94 | 49.11 | 42.74 | 48.68 | 53.84 | 52.47 | |
Zhang_NWPU_task2_1 | ZhangNWPU2025 | 103 | 51.635 | 67.49 | 94.18 | 52.42 | 63.22 | 55.47 | 72.86 | 50.11 | 50.80 | 58.00 | 82.26 | 56.42 | 49.86 | 55.58 | 79.62 | 58.74 | 71.88 | 48.42 | 40.29 | 26.64 | 52.42 | 66.00 | 55.47 | 41.44 | 50.11 | 65.90 | 58.00 | 50.86 | 56.42 | 70.64 | 55.58 | 44.52 | 58.74 | 19.88 | 48.42 | |
Zhang_NWPU_task2_2 | ZhangNWPU2025 | 67 | 55.425 | 70.47 | 94.62 | 56.95 | 66.88 | 55.95 | 74.26 | 50.84 | 56.58 | 55.11 | 82.30 | 58.32 | 54.14 | 57.53 | 79.10 | 58.32 | 73.56 | 47.95 | 46.05 | 50.44 | 56.95 | 62.24 | 55.95 | 39.26 | 50.84 | 54.56 | 55.11 | 50.62 | 58.32 | 73.30 | 57.53 | 51.62 | 58.32 | 24.24 | 47.95 | |
Zhang_NWPU_task2_3 | ZhangNWPU2025 | 75 | 54.536 | 69.90 | 95.54 | 57.68 | 64.82 | 54.68 | 74.56 | 51.05 | 54.12 | 56.00 | 83.06 | 59.26 | 53.62 | 57.47 | 79.68 | 58.42 | 74.52 | 47.63 | 44.40 | 47.44 | 57.68 | 63.60 | 54.68 | 39.20 | 51.05 | 57.14 | 56.00 | 50.68 | 59.26 | 73.96 | 57.47 | 49.80 | 58.42 | 21.26 | 47.63 | |
Zhang_NWPU_task2_4 | ZhangNWPU2025 | 82 | 54.279 | 69.37 | 95.66 | 58.58 | 64.80 | 55.16 | 74.28 | 51.05 | 53.80 | 55.63 | 83.30 | 58.63 | 51.60 | 56.37 | 80.14 | 58.53 | 73.92 | 47.68 | 44.15 | 50.04 | 58.58 | 63.48 | 55.16 | 38.92 | 51.05 | 55.10 | 55.63 | 48.78 | 58.63 | 65.82 | 56.37 | 49.00 | 58.53 | 21.96 | 47.68 | |
Chao_BUCT_task2_1 | ChaoBUCT2025 | 118 | 47.243 | 42.02 | 57.60 | 52.68 | 30.46 | 49.79 | 77.04 | 51.47 | 37.06 | 49.11 | 50.40 | 51.79 | 26.34 | 48.58 | 38.66 | 49.26 | 60.44 | 49.68 | 50.49 | 57.22 | 52.68 | 67.24 | 49.79 | 60.16 | 51.47 | 71.68 | 49.11 | 60.32 | 51.79 | 38.68 | 48.58 | 53.76 | 49.26 | 28.98 | 49.68 | |
Chao_BUCT_task2_2 | ChaoBUCT2025 | 114 | 48.452 | 47.37 | 53.89 | 48.92 | 59.06 | 50.69 | 44.93 | 48.91 | 28.30 | 51.92 | 53.40 | 52.39 | 69.06 | 51.62 | 53.05 | 49.23 | 42.09 | 49.65 | 47.72 | 53.04 | 48.92 | 35.86 | 50.69 | 39.80 | 48.91 | 56.69 | 51.92 | 53.19 | 52.39 | 55.78 | 51.62 | 49.02 | 49.23 | 47.58 | 49.65 | |
Chao_BUCT_task2_3 | ChaoBUCT2025 | 108 | 50.132 | 58.27 | 62.66 | 50.26 | 73.82 | 48.58 | 74.90 | 50.95 | 57.36 | 52.11 | 51.63 | 51.79 | 52.31 | 48.63 | 65.52 | 48.47 | 43.00 | 49.63 | 44.08 | 51.84 | 50.26 | 25.04 | 48.58 | 66.72 | 50.95 | 64.32 | 52.11 | 53.98 | 51.79 | 34.10 | 48.63 | 42.30 | 48.47 | 49.40 | 49.63 | |
Li_XJTLU_task2_1 | LiXJTLU2025 | 89 | 53.298 | 64.40 | 83.48 | 68.11 | 56.04 | 51.53 | 71.32 | 52.05 | 79.82 | 50.05 | 60.38 | 50.95 | 56.54 | 50.05 | 74.02 | 51.00 | 49.78 | 49.79 | 46.11 | 70.22 | 68.11 | 42.76 | 51.53 | 30.28 | 52.05 | 42.24 | 50.05 | 60.04 | 50.95 | 52.32 | 50.05 | 41.26 | 51.00 | 52.18 | 49.79 | |
Li_XJTLU_task2_2 | LiXJTLU2025 | 87 | 53.469 | 62.69 | 83.48 | 68.11 | 52.94 | 51.11 | 71.32 | 52.05 | 52.78 | 53.63 | 73.18 | 50.00 | 56.54 | 50.05 | 74.02 | 51.00 | 52.88 | 49.89 | 47.19 | 70.22 | 68.11 | 51.84 | 51.11 | 30.28 | 52.05 | 53.80 | 53.63 | 41.04 | 50.00 | 52.32 | 50.05 | 41.26 | 51.00 | 60.06 | 49.89 | |
Li_XJTLU_task2_3 | LiXJTLU2025 | 106 | 50.876 | 62.37 | 66.94 | 57.74 | 69.56 | 51.63 | 70.64 | 52.37 | 52.92 | 48.37 | 72.88 | 49.95 | 50.30 | 48.37 | 73.66 | 54.42 | 53.42 | 50.26 | 42.54 | 68.56 | 57.74 | 34.74 | 51.63 | 36.64 | 52.37 | 41.06 | 48.37 | 34.44 | 49.95 | 41.96 | 48.37 | 48.70 | 54.42 | 50.96 | 50.26 | |
Li_XJTLU_task2_4 | LiXJTLU2025 | 99 | 52.523 | 66.93 | 86.98 | 58.16 | 78.04 | 52.42 | 57.24 | 48.42 | 59.52 | 50.63 | 54.88 | 49.21 | 76.46 | 68.00 | 76.18 | 54.42 | 60.56 | 50.63 | 42.64 | 50.44 | 58.16 | 35.28 | 52.42 | 27.60 | 48.42 | 39.96 | 50.63 | 52.26 | 49.21 | 79.66 | 68.00 | 45.44 | 54.42 | 40.86 | 50.63 | |
Wang_ZJU_task2_1 | WangZJU2025 | 107 | 50.229 | 65.41 | 81.54 | 48.53 | 71.14 | 52.16 | 72.30 | 51.95 | 73.90 | 51.21 | 64.38 | 52.11 | 59.24 | 60.37 | 58.96 | 54.95 | 51.96 | 56.16 | 38.99 | 21.00 | 48.53 | 41.12 | 52.16 | 41.54 | 51.95 | 32.68 | 51.21 | 48.22 | 52.11 | 57.12 | 60.37 | 39.80 | 54.95 | 65.78 | 56.16 | |
Wang_ZJU_task2_2 | WangZJU2025 | 64 | 55.589 | 68.93 | 73.50 | 53.32 | 75.60 | 50.89 | 73.76 | 50.37 | 80.10 | 51.58 | 62.92 | 53.68 | 73.96 | 68.47 | 63.80 | 55.00 | 55.28 | 53.95 | 47.59 | 52.54 | 53.32 | 34.86 | 50.89 | 38.50 | 50.37 | 35.62 | 51.58 | 57.60 | 53.68 | 75.60 | 68.47 | 50.54 | 55.00 | 62.70 | 53.95 | |
Wang_ZJU_task2_3 | WangZJU2025 | 85 | 53.763 | 69.17 | 90.16 | 53.16 | 69.56 | 52.16 | 73.64 | 54.05 | 73.50 | 53.42 | 67.56 | 50.84 | 64.70 | 60.74 | 68.06 | 54.63 | 55.40 | 56.21 | 43.64 | 33.26 | 53.16 | 40.96 | 52.16 | 45.08 | 54.05 | 41.28 | 53.42 | 45.14 | 50.84 | 61.30 | 60.74 | 35.48 | 54.63 | 63.36 | 56.21 | |
Wang_ZJU_task2_4 | WangZJU2025 | 55 | 56.866 | 71.72 | 82.42 | 54.53 | 74.44 | 50.05 | 75.68 | 51.84 | 80.42 | 52.32 | 66.80 | 55.00 | 69.82 | 67.58 | 69.54 | 57.00 | 60.02 | 54.84 | 48.48 | 50.22 | 54.53 | 34.36 | 50.05 | 41.52 | 51.84 | 42.46 | 52.32 | 52.90 | 55.00 | 74.94 | 67.58 | 51.46 | 57.00 | 59.94 | 54.84 | |
Lin_IASP_task2_1 | LinIASP2025 | 105 | 51.076 | 56.54 | 62.84 | 49.47 | 74.78 | 51.32 | 51.58 | 52.89 | 58.64 | 50.84 | 49.84 | 51.05 | 53.58 | 52.89 | 55.64 | 51.89 | 52.44 | 48.32 | 46.61 | 52.08 | 49.47 | 29.02 | 51.32 | 51.28 | 52.89 | 52.72 | 50.84 | 45.74 | 51.05 | 56.78 | 52.89 | 46.96 | 51.89 | 53.32 | 48.32 | |
Lin_IASP_task2_2 | LinIASP2025 | 78 | 54.420 | 59.70 | 93.88 | 73.00 | 67.40 | 51.16 | 59.14 | 50.11 | 54.60 | 49.42 | 68.64 | 53.05 | 56.84 | 58.79 | 54.18 | 52.84 | 44.10 | 51.95 | 50.15 | 75.84 | 73.00 | 36.98 | 51.16 | 37.24 | 50.11 | 46.70 | 49.42 | 51.16 | 53.05 | 63.58 | 58.79 | 45.56 | 52.84 | 72.36 | 51.95 | |
Lin_IASP_task2_3 | LinIASP2025 | 74 | 54.548 | 63.20 | 93.86 | 71.37 | 74.78 | 51.32 | 70.94 | 48.68 | 57.92 | 53.42 | 68.64 | 53.05 | 57.02 | 61.89 | 56.10 | 53.05 | 47.04 | 54.32 | 47.51 | 74.12 | 71.37 | 29.02 | 51.32 | 40.96 | 48.68 | 42.14 | 53.42 | 51.16 | 53.05 | 65.52 | 61.89 | 41.24 | 53.05 | 75.64 | 54.32 | |
Lin_IASP_task2_4 | LinIASP2025 | 73 | 54.776 | 63.29 | 93.68 | 69.58 | 67.40 | 51.16 | 63.62 | 51.42 | 54.00 | 49.74 | 54.20 | 53.26 | 59.22 | 58.74 | 61.94 | 54.68 | 65.88 | 50.47 | 48.67 | 66.64 | 69.58 | 36.98 | 51.16 | 44.14 | 51.42 | 43.50 | 49.74 | 52.42 | 53.26 | 63.76 | 58.74 | 49.04 | 54.68 | 46.46 | 50.47 | |
Lobanov_ITMO_task2_1 | LobanovITMO2025 | 110 | 49.581 | 51.50 | 84.56 | 49.84 | 55.00 | 51.37 | 46.56 | 51.95 | 48.56 | 50.53 | 56.14 | 51.11 | 48.16 | 49.58 | 40.80 | 49.58 | 49.56 | 54.32 | 46.56 | 26.84 | 49.84 | 50.90 | 51.37 | 47.78 | 51.95 | 53.48 | 50.53 | 56.28 | 51.11 | 50.56 | 49.58 | 46.58 | 49.58 | 61.44 | 54.32 | |
Lobanov_ITMO_task2_2 | LobanovITMO2025 | 98 | 52.526 | 52.41 | 83.66 | 62.26 | 56.88 | 49.89 | 43.02 | 51.74 | 54.82 | 52.89 | 49.76 | 50.42 | 48.32 | 49.74 | 45.80 | 51.21 | 52.68 | 49.16 | 53.28 | 55.14 | 62.26 | 59.16 | 49.89 | 44.68 | 51.74 | 59.78 | 52.89 | 56.64 | 50.42 | 58.38 | 49.74 | 50.26 | 51.21 | 46.92 | 49.16 | |
Qian_nivic_task2_1 | Qiannivic2025 | 32 | 57.835 | 57.06 | 71.54 | 76.00 | 60.26 | 53.47 | 59.26 | 51.58 | 41.12 | 53.32 | 56.82 | 57.21 | 66.42 | 58.47 | 62.08 | 54.53 | 50.84 | 49.11 | 60.84 | 93.76 | 76.00 | 55.22 | 53.47 | 53.48 | 51.58 | 53.94 | 53.32 | 74.58 | 57.21 | 78.78 | 58.47 | 56.72 | 54.53 | 46.00 | 49.11 | |
Qian_nivic_task2_2 | Qiannivic2025 | 28 | 58.020 | 58.29 | 78.09 | 56.10 | 60.26 | 53.47 | 59.26 | 51.58 | 41.12 | 53.32 | 61.22 | 61.26 | 68.76 | 64.47 | 62.08 | 54.53 | 50.84 | 49.11 | 60.98 | 87.47 | 56.10 | 55.22 | 53.47 | 53.48 | 51.58 | 53.94 | 53.32 | 76.80 | 61.26 | 83.16 | 64.47 | 56.72 | 54.53 | 46.00 | 49.11 | |
Qian_nivic_task2_3 | Qiannivic2025 | 49 | 57.130 | 55.47 | 70.68 | 74.32 | 60.26 | 53.47 | 59.26 | 51.58 | 40.20 | 51.53 | 55.82 | 58.84 | 59.78 | 52.00 | 56.50 | 53.05 | 51.60 | 50.95 | 61.43 | 93.36 | 74.32 | 55.22 | 53.47 | 53.48 | 51.58 | 55.16 | 51.53 | 73.48 | 58.84 | 79.72 | 52.00 | 56.48 | 53.05 | 48.26 | 50.95 | |
Qian_nivic_task2_4 | Qiannivic2025 | 39 | 57.666 | 56.93 | 76.71 | 56.10 | 60.26 | 53.47 | 59.26 | 51.58 | 40.20 | 51.53 | 60.26 | 63.37 | 64.60 | 62.26 | 56.50 | 53.05 | 51.60 | 50.95 | 61.53 | 87.33 | 56.10 | 55.22 | 53.47 | 53.48 | 51.58 | 55.16 | 51.53 | 76.10 | 63.37 | 82.92 | 62.26 | 56.48 | 53.05 | 48.26 | 50.95 | |
Wang_MYPS_task2_1 | WangMYPS2025 | 16 | 59.266 | 56.35 | 68.40 | 72.00 | 58.96 | 50.79 | 61.00 | 53.21 | 38.14 | 49.47 | 62.16 | 51.79 | 91.16 | 85.32 | 63.58 | 53.58 | 40.00 | 52.68 | 65.73 | 96.62 | 72.00 | 56.08 | 50.79 | 56.94 | 53.21 | 61.72 | 49.47 | 68.74 | 51.79 | 97.54 | 85.32 | 58.60 | 53.58 | 55.84 | 52.68 | |
Wang_MYPS_task2_2 | WangMYPS2025 | 20 | 58.804 | 57.22 | 79.57 | 56.10 | 58.96 | 50.79 | 61.00 | 53.21 | 38.14 | 49.47 | 65.58 | 55.16 | 85.55 | 71.87 | 63.58 | 53.58 | 40.00 | 52.68 | 65.51 | 88.05 | 56.10 | 56.08 | 50.79 | 56.94 | 53.21 | 61.72 | 49.47 | 71.72 | 55.16 | 97.54 | 71.87 | 58.60 | 53.58 | 55.84 | 52.68 | |
Wang_MYPS_task2_3 | WangMYPS2025 | 1 | 61.628 | 61.98 | 71.42 | 77.05 | 63.56 | 51.63 | 60.70 | 52.16 | 65.36 | 53.79 | 64.16 | 54.05 | 90.42 | 79.16 | 63.94 | 54.05 | 39.56 | 52.84 | 65.74 | 92.52 | 77.05 | 64.90 | 51.63 | 55.42 | 52.16 | 59.78 | 53.79 | 74.08 | 54.05 | 90.06 | 79.16 | 61.40 | 54.05 | 50.46 | 52.84 | |
Wang_MYPS_task2_4 | WangMYPS2025 | 4 | 61.042 | 62.86 | 80.21 | 56.10 | 63.56 | 51.63 | 60.70 | 52.16 | 65.36 | 53.79 | 68.56 | 58.47 | 84.76 | 67.89 | 63.94 | 54.05 | 39.56 | 52.84 | 65.74 | 88.13 | 56.10 | 64.90 | 51.63 | 55.42 | 52.16 | 59.78 | 53.79 | 77.20 | 58.47 | 90.08 | 67.89 | 61.40 | 54.05 | 50.46 | 52.84 | |
Emon_HDK_task2_1 | EmonHDK2025 | 121 | 45.151 | 40.08 | 19.20 | 52.26 | 72.60 | 48.47 | 66.32 | 52.21 | 38.04 | 52.68 | 43.05 | 49.58 | 51.87 | 48.58 | 38.90 | 48.32 | 41.42 | 51.84 | 46.16 | 74.04 | 52.26 | 23.38 | 48.47 | 75.60 | 52.21 | 59.44 | 52.68 | 44.78 | 49.58 | 55.27 | 48.58 | 36.71 | 48.32 | 51.72 | 51.84 | |
Fu_CUMT_task2_1 | FuCUMT2025 | 52 | 57.023 | 55.18 | 70.34 | 77.16 | 57.32 | 56.11 | 56.56 | 50.79 | 41.16 | 49.32 | 58.56 | 58.21 | 60.84 | 52.79 | 56.92 | 55.37 | 49.36 | 50.11 | 61.06 | 94.18 | 77.16 | 57.12 | 56.11 | 53.22 | 50.79 | 60.30 | 49.32 | 71.90 | 58.21 | 82.42 | 52.79 | 54.50 | 55.37 | 43.24 | 50.11 | |
Fu_CUMT_task2_2 | FuCUMT2025 | 40 | 57.585 | 56.79 | 76.99 | 56.10 | 57.32 | 56.11 | 56.56 | 50.79 | 41.16 | 49.32 | 64.06 | 62.95 | 66.56 | 63.26 | 56.92 | 55.37 | 49.36 | 50.11 | 61.27 | 87.87 | 56.10 | 57.12 | 56.11 | 53.22 | 50.79 | 60.30 | 49.32 | 75.74 | 62.95 | 85.92 | 63.26 | 54.50 | 55.37 | 43.24 | 50.11 | |
Fu_CUMT_task2_3 | FuCUMT2025 | 41 | 57.564 | 54.96 | 70.34 | 77.16 | 51.68 | 54.95 | 61.18 | 57.53 | 41.16 | 49.32 | 58.56 | 58.21 | 60.84 | 52.79 | 56.92 | 55.37 | 49.36 | 50.11 | 62.30 | 94.18 | 77.16 | 58.92 | 54.95 | 59.78 | 57.53 | 60.30 | 49.32 | 71.90 | 58.21 | 82.42 | 52.79 | 54.50 | 55.37 | 43.24 | 50.11 | |
Fu_CUMT_task2_4 | FuCUMT2025 | 26 | 58.137 | 56.56 | 76.99 | 56.10 | 51.68 | 54.95 | 61.18 | 57.53 | 41.16 | 49.32 | 64.06 | 62.95 | 66.56 | 63.26 | 56.92 | 55.37 | 49.36 | 50.11 | 62.51 | 87.87 | 56.10 | 58.92 | 54.95 | 59.78 | 57.53 | 60.30 | 49.32 | 75.74 | 62.95 | 85.92 | 63.26 | 54.50 | 55.37 | 43.24 | 50.11 | |
Ding_HFUU_task2_1 | DingHFUU2025 | 71 | 55.130 | 68.58 | 91.26 | 65.89 | 71.94 | 51.00 | 73.66 | 51.53 | 67.92 | 55.16 | 70.30 | 54.05 | 64.24 | 56.89 | 72.58 | 53.47 | 50.18 | 54.58 | 46.17 | 57.10 | 65.89 | 32.50 | 51.00 | 40.60 | 51.53 | 41.40 | 55.16 | 52.68 | 54.05 | 69.76 | 56.89 | 34.52 | 53.47 | 71.92 | 54.58 | |
Ding_HFUU_task2_2 | DingHFUU2025 | 81 | 54.303 | 69.05 | 89.60 | 60.74 | 73.36 | 50.21 | 73.50 | 50.89 | 69.70 | 55.05 | 72.14 | 54.26 | 74.64 | 58.47 | 70.16 | 51.37 | 46.28 | 52.58 | 44.97 | 51.54 | 60.74 | 30.40 | 50.21 | 46.30 | 50.89 | 42.66 | 55.05 | 46.96 | 54.26 | 69.42 | 58.47 | 32.96 | 51.37 | 68.92 | 52.58 | |
Ding_HFUU_task2_3 | DingHFUU2025 | 91 | 53.158 | 68.29 | 87.12 | 52.42 | 70.42 | 51.58 | 73.26 | 52.84 | 76.52 | 52.68 | 66.70 | 50.79 | 61.98 | 59.47 | 67.46 | 54.89 | 53.18 | 55.74 | 43.17 | 31.84 | 52.42 | 40.92 | 51.58 | 43.58 | 52.84 | 41.72 | 52.68 | 44.26 | 50.79 | 61.22 | 59.47 | 36.20 | 54.89 | 62.56 | 55.74 | |
Ding_HFUU_task2_4 | DingHFUU2025 | 59 | 56.378 | 72.08 | 85.54 | 51.95 | 76.26 | 49.47 | 75.56 | 52.11 | 81.50 | 52.21 | 67.32 | 53.89 | 68.90 | 67.84 | 69.46 | 57.32 | 59.14 | 54.32 | 47.69 | 53.70 | 51.95 | 33.52 | 49.47 | 41.32 | 52.11 | 38.92 | 52.21 | 52.06 | 53.89 | 71.68 | 67.84 | 51.68 | 57.32 | 59.18 | 54.32 | |
Yang_NBU_task2_1 | YangNBU2025 | 3 | 61.201 | 65.32 | 76.16 | 75.37 | 60.72 | 57.79 | 53.80 | 49.63 | 64.42 | 53.37 | 75.52 | 61.32 | 92.02 | 80.00 | 69.42 | 53.21 | 49.36 | 49.74 | 60.38 | 95.56 | 75.37 | 73.28 | 57.79 | 46.86 | 49.63 | 61.62 | 53.37 | 75.76 | 61.32 | 96.52 | 80.00 | 48.10 | 53.21 | 37.78 | 49.74 | |
Yang_NBU_task2_2 | YangNBU2025 | 6 | 60.447 | 65.67 | 81.01 | 56.10 | 60.72 | 57.79 | 53.80 | 49.63 | 64.42 | 53.37 | 78.62 | 65.84 | 86.83 | 70.03 | 69.42 | 53.21 | 49.36 | 49.74 | 60.22 | 88.35 | 56.10 | 73.28 | 57.79 | 46.86 | 49.63 | 61.62 | 53.37 | 78.56 | 65.84 | 96.96 | 70.03 | 48.10 | 53.21 | 37.78 | 49.74 | |
Yang_NBU_task2_3 | YangNBU2025 | 5 | 60.950 | 63.02 | 73.86 | 80.21 | 61.72 | 55.21 | 57.10 | 51.32 | 67.26 | 53.58 | 75.54 | 60.58 | 92.32 | 80.79 | 64.62 | 53.63 | 39.56 | 50.05 | 61.27 | 96.28 | 80.21 | 67.20 | 55.21 | 53.10 | 51.32 | 62.18 | 53.58 | 75.38 | 60.58 | 92.88 | 80.79 | 48.52 | 53.63 | 38.83 | 50.05 | |
Yang_NBU_task2_4 | YangNBU2025 | 8 | 59.924 | 63.30 | 79.67 | 56.10 | 61.72 | 55.21 | 57.10 | 51.32 | 67.26 | 53.58 | 77.42 | 63.89 | 86.42 | 68.53 | 64.62 | 53.63 | 39.56 | 50.05 | 60.99 | 88.29 | 56.10 | 67.20 | 55.21 | 53.10 | 51.32 | 62.18 | 53.58 | 77.36 | 63.89 | 92.94 | 68.53 | 48.52 | 53.63 | 38.83 | 50.05 | |
Kret_CU_task2_1 | KretCU2025 | 115 | 47.903 | 63.05 | 70.30 | 48.42 | 55.58 | 50.32 | 57.20 | 49.26 | 72.78 | 49.63 | 57.52 | 51.21 | 59.68 | 62.79 | 73.32 | 54.74 | 63.84 | 49.79 | 36.47 | 21.22 | 48.42 | 46.02 | 50.32 | 37.76 | 49.26 | 21.34 | 49.63 | 47.40 | 51.21 | 62.46 | 62.79 | 45.76 | 54.74 | 54.98 | 49.79 | |
Zheng_SJTU-AITHU_task2_1 | ZhengSJTU-AITHU2025 | 13 | 59.370 | 60.48 | 88.30 | 79.74 | 64.26 | 58.53 | 54.06 | 47.53 | 54.70 | 55.58 | 64.68 | 54.74 | 64.16 | 51.26 | 71.14 | 57.00 | 42.56 | 52.11 | 62.11 | 94.56 | 79.74 | 72.38 | 58.53 | 50.50 | 47.53 | 60.84 | 55.58 | 57.40 | 54.74 | 66.08 | 51.26 | 57.08 | 57.00 | 55.24 | 52.11 | |
Zheng_SJTU-AITHU_task2_2 | ZhengSJTU-AITHU2025 | 11 | 59.500 | 58.73 | 92.42 | 85.58 | 61.80 | 58.95 | 56.38 | 47.58 | 50.60 | 55.32 | 59.34 | 54.37 | 67.52 | 53.21 | 73.46 | 56.00 | 37.82 | 52.95 | 63.76 | 96.88 | 85.58 | 75.80 | 58.95 | 48.72 | 47.58 | 63.48 | 55.32 | 57.14 | 54.37 | 66.90 | 53.21 | 57.06 | 56.00 | 63.68 | 52.95 | |
Zheng_SJTU-AITHU_task2_3 | ZhengSJTU-AITHU2025 | 15 | 59.314 | 58.30 | 93.54 | 86.79 | 60.72 | 57.89 | 55.56 | 47.42 | 50.42 | 55.05 | 58.00 | 54.68 | 66.50 | 53.21 | 73.58 | 55.89 | 37.92 | 52.74 | 63.79 | 96.94 | 86.79 | 76.88 | 57.89 | 47.56 | 47.42 | 63.50 | 55.05 | 57.68 | 54.68 | 66.56 | 53.21 | 56.04 | 55.89 | 66.18 | 52.74 | |
Zheng_SJTU-AITHU_task2_4 | ZhengSJTU-AITHU2025 | 12 | 59.441 | 58.52 | 92.44 | 85.16 | 61.50 | 58.47 | 56.42 | 47.58 | 50.50 | 55.00 | 58.74 | 54.37 | 67.18 | 53.58 | 73.28 | 55.53 | 37.68 | 52.95 | 63.97 | 96.98 | 85.16 | 75.92 | 58.47 | 48.82 | 47.58 | 64.30 | 55.00 | 56.92 | 54.37 | 66.94 | 53.58 | 56.82 | 55.53 | 64.78 | 52.95 | |
Zhao_CUMT_task2_1 | ZhaoCUMT2025 | 102 | 51.655 | 53.52 | 66.12 | 61.42 | 58.96 | 51.95 | 41.08 | 49.26 | 55.48 | 49.11 | 52.95 | 48.47 | 43.94 | 52.00 | 65.52 | 54.95 | 55.16 | 51.00 | 49.61 | 64.48 | 61.42 | 48.32 | 51.95 | 44.84 | 49.26 | 48.40 | 49.11 | 35.27 | 48.47 | 62.30 | 52.00 | 57.78 | 54.95 | 49.04 | 51.00 | |
Zhao_CUMT_task2_2 | ZhaoCUMT2025 | 101 | 52.094 | 53.98 | 68.56 | 61.26 | 53.40 | 50.84 | 61.78 | 49.53 | 56.00 | 49.74 | 50.64 | 50.11 | 40.64 | 51.11 | 61.62 | 55.68 | 49.32 | 49.79 | 50.43 | 56.50 | 61.26 | 48.18 | 50.84 | 51.56 | 49.53 | 36.04 | 49.74 | 45.36 | 50.11 | 75.02 | 51.11 | 55.21 | 55.68 | 51.14 | 49.79 | |
Zhao_CUMT_task2_3 | ZhaoCUMT2025 | 61 | 56.057 | 68.15 | 58.68 | 51.05 | 75.22 | 50.89 | 76.74 | 50.26 | 73.80 | 53.37 | 70.10 | 56.84 | 77.80 | 68.63 | 59.48 | 54.26 | 60.46 | 54.21 | 48.83 | 61.06 | 51.05 | 35.52 | 50.89 | 39.02 | 50.26 | 39.80 | 53.37 | 56.60 | 56.84 | 82.46 | 68.63 | 45.86 | 54.26 | 58.98 | 54.21 | |
Zhao_CUMT_task2_4 | ZhaoCUMT2025 | 62 | 55.784 | 64.73 | 64.12 | 51.95 | 74.68 | 51.63 | 66.86 | 51.37 | 82.94 | 52.42 | 55.22 | 51.63 | 65.48 | 66.37 | 64.18 | 55.74 | 53.66 | 54.00 | 50.45 | 63.32 | 51.95 | 39.02 | 51.63 | 39.26 | 51.37 | 38.42 | 52.42 | 59.22 | 51.63 | 74.40 | 66.37 | 50.46 | 55.74 | 64.48 | 54.00 | |
Ozeki_MELCO_task2_1 | OzekiMELCO2025 | 23 | 58.234 | 66.73 | 85.18 | 74.79 | 67.36 | 55.79 | 71.48 | 51.05 | 57.84 | 50.63 | 73.04 | 55.63 | 53.22 | 49.42 | 73.48 | 56.89 | 62.74 | 55.11 | 54.17 | 87.56 | 74.79 | 57.50 | 55.79 | 34.30 | 51.05 | 55.22 | 50.63 | 54.46 | 55.63 | 51.42 | 49.42 | 56.90 | 56.89 | 61.68 | 55.11 | |
Ozeki_MELCO_task2_2 | OzekiMELCO2025 | 60 | 56.122 | 63.37 | 83.72 | 71.00 | 64.14 | 53.58 | 71.02 | 48.95 | 52.70 | 50.42 | 72.60 | 55.53 | 50.92 | 49.74 | 74.60 | 54.79 | 53.10 | 51.89 | 52.37 | 88.92 | 71.00 | 52.70 | 53.58 | 31.24 | 48.95 | 56.78 | 50.42 | 51.58 | 55.53 | 53.00 | 49.74 | 56.14 | 54.79 | 59.38 | 51.89 | |
Ozeki_MELCO_task2_3 | OzekiMELCO2025 | 69 | 55.296 | 63.57 | 83.32 | 70.26 | 64.14 | 53.58 | 69.52 | 49.74 | 55.10 | 48.37 | 72.60 | 55.53 | 50.92 | 49.74 | 74.20 | 55.00 | 53.10 | 51.89 | 50.33 | 88.72 | 70.26 | 52.70 | 53.58 | 28.12 | 49.74 | 49.26 | 48.37 | 51.58 | 55.53 | 53.00 | 49.74 | 56.40 | 55.00 | 59.38 | 51.89 | |
Ozeki_MELCO_task2_4 | OzekiMELCO2025 | 79 | 54.407 | 62.80 | 85.30 | 58.00 | 62.92 | 53.63 | 70.44 | 52.58 | 60.14 | 50.63 | 73.02 | 53.68 | 46.36 | 49.11 | 71.52 | 50.00 | 50.74 | 51.84 | 49.76 | 64.36 | 58.00 | 59.20 | 53.63 | 35.80 | 52.58 | 51.00 | 50.63 | 51.08 | 53.68 | 46.86 | 49.11 | 51.76 | 50.00 | 48.60 | 51.84 | |
Huang_XJU_task2_1 | HuangXJU2025 | 25 | 58.141 | 68.60 | 76.72 | 53.63 | 64.98 | 59.37 | 74.72 | 49.21 | 69.64 | 52.79 | 61.63 | 50.42 | 76.74 | 69.32 | 77.02 | 57.68 | 54.92 | 51.00 | 53.26 | 71.48 | 53.63 | 51.00 | 59.37 | 34.26 | 49.21 | 62.14 | 52.79 | 45.10 | 50.42 | 88.36 | 69.32 | 51.58 | 57.68 | 54.16 | 51.00 | |
Huang_XJU_task2_2 | HuangXJU2025 | 27 | 58.070 | 66.34 | 58.74 | 49.37 | 68.52 | 56.05 | 87.26 | 51.63 | 64.84 | 51.89 | 65.16 | 50.32 | 73.30 | 63.11 | 66.74 | 59.05 | 55.20 | 52.16 | 55.50 | 57.08 | 49.37 | 47.90 | 56.05 | 56.38 | 51.63 | 59.86 | 51.89 | 42.84 | 50.32 | 84.36 | 63.11 | 53.46 | 59.05 | 57.46 | 52.16 | |
Huang_XJU_task2_3 | HuangXJU2025 | 35 | 57.739 | 67.57 | 72.54 | 52.26 | 68.58 | 56.16 | 75.16 | 49.42 | 62.86 | 51.95 | 67.82 | 49.95 | 74.94 | 65.68 | 73.45 | 58.32 | 52.32 | 54.11 | 53.35 | 67.28 | 52.26 | 50.68 | 56.16 | 36.34 | 49.42 | 63.64 | 51.95 | 43.40 | 49.95 | 88.24 | 65.68 | 48.42 | 58.32 | 58.44 | 54.11 | |
Huang_XJU_task2_4 | HuangXJU2025 | 37 | 57.706 | 63.50 | 55.60 | 48.53 | 59.00 | 58.68 | 86.30 | 52.21 | 58.10 | 52.26 | 68.64 | 50.95 | 76.48 | 68.11 | 64.22 | 57.00 | 52.54 | 52.16 | 55.96 | 45.38 | 48.53 | 57.18 | 58.68 | 56.24 | 52.21 | 65.14 | 52.26 | 44.56 | 50.95 | 84.94 | 68.11 | 56.48 | 57.00 | 54.42 | 52.16 | |
Fujimura_NU_task2_1 | FujimuraNU2025 | 7 | 59.995 | 75.36 | 99.30 | 65.53 | 74.40 | 61.84 | 72.14 | 52.00 | 67.84 | 53.68 | 76.50 | 58.47 | 74.92 | 68.89 | 79.70 | 57.05 | 66.34 | 51.89 | 51.22 | 64.36 | 65.53 | 74.14 | 61.84 | 38.68 | 52.00 | 61.76 | 53.68 | 51.82 | 58.47 | 95.16 | 68.89 | 44.76 | 57.05 | 30.34 | 51.89 | |
Fujimura_NU_task2_2 | FujimuraNU2025 | 21 | 58.510 | 71.82 | 95.26 | 61.11 | 63.08 | 58.95 | 75.88 | 50.89 | 75.96 | 55.21 | 62.88 | 51.37 | 80.68 | 66.11 | 65.58 | 58.00 | 65.98 | 51.00 | 51.20 | 74.62 | 61.11 | 74.84 | 58.95 | 36.14 | 50.89 | 53.68 | 55.21 | 44.40 | 51.37 | 84.02 | 66.11 | 50.86 | 58.00 | 34.36 | 51.00 | |
Fujimura_NU_task2_3 | FujimuraNU2025 | 14 | 59.343 | 77.44 | 97.86 | 64.68 | 75.36 | 61.89 | 75.86 | 49.58 | 73.60 | 54.95 | 77.20 | 54.95 | 82.80 | 73.05 | 76.84 | 58.16 | 66.64 | 51.53 | 49.19 | 58.18 | 64.68 | 71.74 | 61.89 | 36.22 | 49.58 | 55.72 | 54.95 | 45.60 | 54.95 | 97.36 | 73.05 | 46.42 | 58.16 | 31.04 | 51.53 | |
Fujimura_NU_task2_4 | FujimuraNU2025 | 9 | 59.908 | 75.83 | 99.22 | 65.00 | 70.66 | 60.84 | 74.34 | 50.89 | 70.90 | 55.26 | 77.78 | 55.00 | 78.04 | 67.95 | 79.20 | 58.89 | 64.86 | 52.16 | 51.12 | 61.02 | 65.00 | 73.30 | 60.84 | 36.54 | 50.89 | 59.44 | 55.26 | 49.50 | 55.00 | 98.02 | 67.95 | 44.98 | 58.89 | 33.74 | 52.16 | |
Jiang_THUEE_task2_1 | JiangTHUEE2025 | 19 | 58.892 | 58.01 | 93.42 | 86.37 | 58.90 | 56.95 | 55.24 | 47.53 | 50.40 | 54.68 | 56.34 | 53.95 | 66.70 | 53.79 | 72.28 | 55.79 | 38.90 | 52.47 | 62.97 | 96.96 | 86.37 | 77.58 | 56.95 | 47.58 | 47.53 | 63.72 | 54.68 | 55.38 | 53.95 | 65.70 | 53.79 | 54.80 | 55.79 | 64.10 | 52.47 | |
Jiang_THUEE_task2_2 | JiangTHUEE2025 | 10 | 59.793 | 59.48 | 92.46 | 86.21 | 60.50 | 58.26 | 57.46 | 47.37 | 50.80 | 55.89 | 61.60 | 54.68 | 68.98 | 56.89 | 73.98 | 56.37 | 38.72 | 51.47 | 63.45 | 97.64 | 86.21 | 77.48 | 58.26 | 48.60 | 47.37 | 59.42 | 55.89 | 57.80 | 54.68 | 71.30 | 56.89 | 57.42 | 56.37 | 59.58 | 51.47 | |
Jiang_THUEE_task2_3 | JiangTHUEE2025 | 18 | 59.074 | 58.13 | 93.56 | 87.11 | 58.22 | 56.00 | 56.56 | 47.42 | 50.00 | 56.16 | 59.14 | 54.37 | 68.18 | 54.53 | 73.52 | 56.00 | 37.16 | 51.84 | 63.23 | 96.98 | 87.11 | 78.84 | 56.00 | 48.08 | 47.42 | 60.26 | 56.16 | 55.30 | 54.37 | 68.64 | 54.53 | 56.78 | 56.00 | 63.00 | 51.84 | |
Jiang_THUEE_task2_4 | JiangTHUEE2025 | 17 | 59.156 | 58.13 | 93.94 | 87.58 | 57.02 | 56.32 | 56.26 | 47.37 | 49.82 | 55.63 | 59.18 | 54.21 | 68.40 | 55.68 | 73.82 | 55.74 | 37.68 | 52.21 | 63.33 | 97.68 | 87.58 | 79.42 | 56.32 | 47.88 | 47.37 | 59.48 | 55.63 | 56.46 | 54.21 | 70.16 | 55.68 | 55.80 | 55.74 | 62.88 | 52.21 | |
Bian_TGU_task2_1 | BianTGU2025 | 119 | 46.576 | 40.69 | 42.08 | 50.21 | 37.36 | 49.47 | 41.96 | 48.63 | 28.78 | 48.37 | 44.62 | 51.05 | 46.16 | 48.68 | 45.76 | 52.42 | 46.32 | 50.95 | 50.48 | 54.04 | 50.21 | 64.12 | 49.47 | 46.72 | 48.63 | 62.08 | 48.37 | 45.42 | 51.05 | 48.96 | 48.68 | 51.96 | 52.42 | 39.74 | 50.95 | |
Bian_TGU_task2_2 | BianTGU2025 | 116 | 47.851 | 48.64 | 41.56 | 49.00 | 68.62 | 50.32 | 51.56 | 51.42 | 47.92 | 49.16 | 46.56 | 50.95 | 45.12 | 52.53 | 51.96 | 49.74 | 44.08 | 49.05 | 44.98 | 41.82 | 49.00 | 28.22 | 50.32 | 46.70 | 51.42 | 51.86 | 49.16 | 52.90 | 50.95 | 51.24 | 52.53 | 54.86 | 49.74 | 47.24 | 49.05 | |
Bian_TGU_task2_3 | BianTGU2025 | 104 | 51.417 | 52.23 | 44.06 | 49.79 | 57.54 | 50.89 | 53.98 | 51.05 | 58.20 | 51.63 | 49.02 | 48.16 | 52.72 | 51.84 | 53.10 | 51.68 | 52.16 | 51.53 | 51.24 | 44.44 | 49.79 | 52.92 | 50.89 | 45.30 | 51.05 | 56.52 | 51.63 | 43.98 | 48.16 | 62.92 | 51.84 | 51.82 | 51.68 | 58.76 | 51.53 | |
Bian_TGU_task2_4 | BianTGU2025 | 120 | 46.193 | 50.08 | 45.74 | 49.79 | 76.74 | 49.58 | 46.58 | 47.58 | 62.70 | 49.74 | 44.14 | 48.95 | 44.78 | 50.11 | 45.98 | 52.16 | 48.28 | 49.26 | 40.29 | 52.94 | 49.79 | 24.74 | 49.58 | 33.92 | 47.58 | 40.04 | 49.74 | 45.62 | 48.95 | 46.98 | 50.11 | 47.50 | 52.16 | 48.70 | 49.26 | |
Sera_TMU_task2_1 | SeraTMU2025 | 57 | 56.568 | 72.13 | 62.84 | 52.00 | 77.24 | 54.05 | 84.42 | 53.21 | 65.50 | 50.58 | 76.16 | 49.47 | 77.90 | 67.21 | 77.70 | 55.74 | 62.10 | 51.32 | 48.62 | 53.30 | 52.00 | 40.02 | 54.05 | 47.66 | 53.21 | 42.98 | 50.58 | 36.24 | 49.47 | 89.72 | 67.21 | 48.42 | 55.74 | 58.30 | 51.32 | |
Kim_DAU_task2_1 | KimDAU2025 | 113 | 48.537 | 44.94 | 63.84 | 52.00 | 34.26 | 49.79 | 60.14 | 51.95 | 31.98 | 49.74 | 47.44 | 50.74 | 39.70 | 49.95 | 51.30 | 48.84 | 51.24 | 49.58 | 50.83 | 78.06 | 52.00 | 63.10 | 49.79 | 55.02 | 51.95 | 44.96 | 49.74 | 41.22 | 50.74 | 54.16 | 49.95 | 34.90 | 48.84 | 59.02 | 49.58 | |
Kim_DAU_task2_2 | KimDAU2025 | 111 | 48.966 | 50.52 | 63.80 | 49.16 | 37.92 | 50.84 | 45.90 | 49.89 | 60.62 | 56.05 | 49.96 | 50.11 | 46.02 | 51.95 | 52.14 | 48.11 | 58.44 | 50.37 | 45.97 | 55.08 | 49.16 | 53.08 | 50.84 | 35.54 | 49.89 | 55.16 | 56.05 | 45.44 | 50.11 | 54.66 | 51.95 | 39.70 | 48.11 | 39.56 | 50.37 | |
Wang_UniS_task2_1 | WangUniS2025 | 34 | 57.754 | 62.96 | 85.42 | 72.84 | 69.62 | 55.00 | 73.40 | 51.26 | 52.36 | 54.11 | 75.38 | 53.05 | 47.24 | 52.84 | 79.32 | 57.63 | 47.10 | 53.84 | 55.21 | 87.80 | 72.84 | 50.72 | 55.00 | 38.62 | 51.26 | 72.66 | 54.11 | 42.46 | 53.05 | 75.44 | 52.84 | 53.46 | 57.63 | 53.72 | 53.84 | |
Wang_UniS_task2_2 | WangUniS2025 | 46 | 57.261 | 64.46 | 84.00 | 69.79 | 71.52 | 56.63 | 73.72 | 51.53 | 52.72 | 55.58 | 76.90 | 53.05 | 49.66 | 51.63 | 80.42 | 57.37 | 49.74 | 52.63 | 52.98 | 85.38 | 69.79 | 49.80 | 56.63 | 36.56 | 51.53 | 72.24 | 55.58 | 42.40 | 53.05 | 72.90 | 51.63 | 53.10 | 57.37 | 45.72 | 52.63 | |
Wang_UniS_task2_3 | WangUniS2025 | 53 | 56.975 | 59.99 | 90.14 | 63.47 | 55.16 | 55.58 | 60.92 | 50.05 | 51.12 | 53.89 | 61.86 | 48.84 | 63.14 | 62.42 | 78.86 | 57.63 | 42.62 | 50.16 | 56.41 | 71.32 | 63.47 | 67.34 | 55.58 | 42.16 | 50.05 | 60.96 | 53.89 | 44.10 | 48.84 | 68.94 | 62.42 | 49.18 | 57.63 | 65.28 | 50.16 | |
Wang_UniS_task2_4 | WangUniS2025 | 90 | 53.190 | 59.95 | 80.74 | 70.11 | 66.28 | 51.63 | 62.00 | 52.53 | 62.94 | 51.68 | 61.64 | 52.68 | 39.64 | 47.37 | 72.38 | 52.68 | 53.50 | 53.16 | 47.66 | 55.94 | 70.11 | 42.64 | 51.63 | 40.16 | 52.53 | 31.18 | 51.68 | 48.88 | 52.68 | 93.32 | 47.37 | 44.16 | 52.68 | 63.54 | 53.16 | |
Guan_HEU_task2_1 | GuanHEU2025 | 22 | 58.253 | 72.85 | 84.14 | 59.53 | 68.38 | 53.63 | 75.56 | 49.58 | 69.48 | 50.74 | 77.48 | 56.11 | 81.90 | 77.26 | 80.50 | 61.79 | 55.12 | 51.89 | 49.80 | 61.12 | 59.53 | 46.72 | 53.63 | 33.64 | 49.58 | 38.30 | 50.74 | 51.44 | 56.11 | 99.82 | 77.26 | 50.66 | 61.79 | 56.08 | 51.89 | |
Guan_HEU_task2_2 | GuanHEU2025 | 48 | 57.211 | 72.14 | 76.94 | 53.37 | 68.16 | 52.95 | 75.44 | 50.11 | 70.02 | 50.42 | 77.72 | 56.00 | 80.90 | 75.42 | 79.62 | 61.32 | 55.86 | 52.11 | 48.61 | 55.34 | 53.37 | 45.58 | 52.95 | 32.70 | 50.11 | 37.24 | 50.42 | 50.80 | 56.00 | 99.02 | 75.42 | 51.28 | 61.32 | 56.04 | 52.11 | |
Guan_HEU_task2_3 | GuanHEU2025 | 117 | 47.504 | 64.26 | 95.46 | 49.42 | 74.84 | 52.11 | 65.78 | 49.47 | 56.56 | 55.63 | 54.20 | 49.84 | 62.94 | 56.74 | 79.76 | 50.05 | 47.84 | 50.00 | 35.49 | 20.84 | 49.42 | 29.20 | 52.11 | 27.34 | 49.47 | 65.42 | 55.63 | 44.34 | 49.84 | 71.56 | 56.74 | 32.16 | 50.05 | 42.16 | 50.00 | |
Guan_HEU_task2_4 | GuanHEU2025 | 68 | 55.318 | 73.02 | 86.20 | 50.00 | 73.54 | 54.53 | 74.90 | 49.74 | 69.48 | 52.26 | 76.72 | 55.63 | 80.22 | 72.84 | 81.40 | 59.11 | 53.10 | 51.26 | 44.80 | 42.62 | 50.00 | 39.32 | 54.53 | 28.34 | 49.74 | 43.28 | 52.26 | 48.94 | 55.63 | 99.14 | 72.84 | 46.12 | 59.11 | 52.36 | 51.26 | |
Kim_AISTAT_task2_1 | KimAISTAT2025 | 31 | 57.845 | 60.99 | 87.08 | 77.47 | 41.18 | 54.74 | 78.96 | 55.47 | 78.58 | 61.32 | 65.24 | 51.11 | 54.90 | 52.00 | 82.02 | 55.68 | 41.20 | 50.47 | 56.48 | 93.06 | 77.47 | 82.48 | 54.74 | 37.54 | 55.47 | 63.20 | 61.32 | 41.52 | 51.11 | 64.20 | 52.00 | 53.02 | 55.68 | 56.18 | 50.47 | |
Kim_AISTAT_task2_2 | KimAISTAT2025 | 33 | 57.808 | 60.92 | 87.80 | 78.37 | 40.74 | 54.58 | 78.80 | 55.32 | 78.14 | 61.53 | 64.88 | 50.84 | 55.28 | 51.47 | 81.96 | 54.95 | 41.34 | 51.00 | 56.52 | 93.44 | 78.37 | 82.22 | 54.58 | 37.48 | 55.32 | 63.36 | 61.53 | 41.54 | 50.84 | 64.22 | 51.47 | 53.32 | 54.95 | 56.16 | 51.00 | |
Kim_AISTAT_task2_3 | KimAISTAT2025 | 36 | 57.733 | 60.94 | 89.16 | 78.05 | 40.84 | 54.05 | 78.82 | 55.00 | 76.72 | 61.32 | 64.44 | 51.21 | 55.38 | 50.84 | 81.40 | 54.47 | 41.66 | 51.16 | 56.53 | 93.22 | 78.05 | 81.84 | 54.05 | 37.34 | 55.00 | 63.36 | 61.32 | 41.86 | 51.21 | 65.28 | 50.84 | 52.44 | 54.47 | 56.40 | 51.16 | |
Kim_AISTAT_task2_4 | KimAISTAT2025 | 29 | 57.955 | 61.27 | 87.90 | 77.47 | 41.46 | 54.53 | 79.32 | 55.32 | 78.10 | 61.53 | 65.44 | 51.63 | 55.54 | 51.58 | 81.64 | 55.79 | 41.46 | 50.47 | 56.54 | 92.64 | 77.47 | 82.18 | 54.53 | 37.54 | 55.32 | 63.16 | 61.53 | 42.30 | 51.63 | 65.16 | 51.58 | 52.18 | 55.79 | 55.88 | 50.47 |
System characteristics
Summary of the submitted system characteristics.
Rank |
Submission Code |
Technical Report |
Classifier |
System Complexity |
Acoustic Feature |
Data Augmentation |
Decision Making |
System Embeddings |
Subsystem Conut |
External Data Usage |
Front End System |
---|---|---|---|---|---|---|---|---|---|---|---|
58 | DCASE2025_baseline_task2_MAHALA | DCASE2025baseline2025 | AE | log-mel energies | |||||||
77 | DCASE2025_baseline_task2_MSE | DCASE2025baseline2025 | AE | log-mel energies | |||||||
100 | Zhou_XJU_task2_1 | ZhouXJU2025 | KNN, Arcface | log-mel spectrogram | specaug | BEATs | BEATs | ||||
94 | Zhou_XJU_task2_2 | ZhouXJU2025 | KNN, Arcface | log-mel spectrogram | specaug | BEATs | BEATs | ||||
96 | Zhou_XJU_task2_3 | ZhouXJU2025 | KNN, Arcface | log-mel spectrogram | specaug | BEATs | BEATs | ||||
93 | Zhou_XJU_task2_4 | ZhouXJU2025 | KNN, Arcface | log-mel spectrogram | specaug | BEATs | BEATs | ||||
45 | Cai_NCUT_task2_1 | CaiNCUT2025 | EAT | log-mel energies | |||||||
83 | Cai_NCUT_task2_2 | CaiNCUT2025 | AE | log-mel energies | EAT | ||||||
42 | Cai_NCUT_task2_3 | CaiNCUT2025 | ensemble | log-mel energies | EAT, M2D | ||||||
84 | Saengthong_SCITOK_task2_1 | SaengthongSCITOK2025 | ensemble | log-mel energies | average | 5 | |||||
2 | Saengthong_SCITOK_task2_2 | SaengthongSCITOK2025 | ensemble | log-mel energies | average | 5 | |||||
56 | Saengthong_SCITOK_task2_3 | SaengthongSCITOK2025 | ensemble | log-mel energies | average | BEATs, M2D-CLAP, EAT, SSLAM, CED | 5 | ||||
80 | Saengthong_SCITOK_task2_4 | SaengthongSCITOK2025 | ensemble | log-mel energies | |||||||
88 | Zhang_DKU_task2_1 | ZhangDKU2025 | transformer, AE | raw waveform | noise augmentation | simulation of attribute labels, pre-trained model | |||||
70 | Zhang_DKU_task2_2 | ZhangDKU2025 | transformer, AE, ensemble | log-mel energies, raw waveform | noise augmentation, spectral augmentation | average | BEATs | 3 | simulation of attribute labels, pre-trained model | ||
92 | Zhang_DKU_task2_3 | ZhangDKU2025 | transformer, AE, ensemble | log-mel energies, raw waveform | noise augmentation, spectral augmentation | average | BEATs | 2 | simulation of attribute labels, pre-trained model | ||
63 | Zhang_DKU_task2_4 | ZhangDKU2025 | transformer, AE, ensemble | log-mel energies, raw waveform | noise augmentation, spectral augmentation | average | BEATs | 2 | simulation of attribute labels, pre-trained model | ||
66 | WT_IACAS_task2_1 | WTIACAS2025 | ResNet, ensemble | log-mel energies | BEATs, EAT, SSLAM | ||||||
44 | WT_IACAS_task2_2 | WTIACAS2025 | ResNet, ensemble | log-mel energies | BEATs, EAT, SSLAM | ||||||
86 | WT_IACAS_task2_3 | WTIACAS2025 | ResNet, ensemble | log-mel energies | BEATs, EAT, SSLAM | ||||||
76 | WT_IACAS_task2_4 | WTIACAS2025 | ResNet, ensemble | log-mel energies | BEATs, EAT, SSLAM | ||||||
24 | Zhou_XAUAT_task2_1 | ZhouXAUAT2025 | AE, KNN | log-mel energies | mixup, specaug, smote | ||||||
43 | Zhou_XAUAT_task2_2 | ZhouXAUAT2025 | AE, KNN | log-mel energies | mixup, specaug, smote | ||||||
95 | Zhou_XAUAT_task2_3 | ZhouXAUAT2025 | AE, LOF | log-mel energies | mixup, specaug, smote | ||||||
50 | Zhou_XAUAT_task2_4 | ZhouXAUAT2025 | AE, KNN | log-mel energies | mixup, specaug | ||||||
54 | Zhong_USTC_task2_1 | ZhongUSTC2025 | KNN | log-mel energies | mixup, addnoise | average | 2 | embeddings, pre-trained model | sound separation | ||
51 | Zhong_USTC_task2_2 | ZhongUSTC2025 | KNN | log-mel energies | mixup, addnoise | average | 2 | embeddings, pre-trained model | sound separation | ||
38 | Zhong_USTC_task2_3 | ZhongUSTC2025 | KNN | log-mel energies | mixup, addnoise | average | 2 | embeddings, pre-trained model | sound separation | ||
30 | Zhong_USTC_task2_4 | ZhongUSTC2025 | KNN | log-mel energies | mixup, addnoise | average | 2 | embeddings, pre-trained model | sound separation | ||
109 | Vijayyan_SNUC_task2_1 | VijayyanSNUC2025 | contrastive learning | log-mel energies | RandomResizedCrop, RandomHorizontalFlip, ColorJitter, RandomGrayscale | ||||||
97 | CHUNG_KUCAU_task2_1 | CHUNGKUCAU2025 | pre-trained model, KNN | log mel spectrogram | BEATs | zero padding or truncated to 10 seconds | |||||
65 | CHUNG_KUCAU_task2_2 | CHUNGKUCAU2025 | pre-trained model, KNN | log mel spectrogram | BEATs | zero padding or truncated to 10 seconds | |||||
72 | CHUNG_KUCAU_task2_3 | CHUNGKUCAU2025 | pre-trained model, KNN | log mel spectrogram | BEATs | zero padding or truncated to 10 seconds | |||||
47 | CHUNG_KUCAU_task2_4 | CHUNGKUCAU2025 | pre-trained model, KNN | log mel spectrogram | BEATs | zero padding or truncated to 10 seconds | |||||
112 | Dung_CNTT1PTIT_task2_1 | DungCNTT1PTIT2025 | CNN, Ensemble | FFT, Magnitude Spectrogram | Mixup, SpecAugment, Random Crop/Padding | average | 2 | ||||
103 | Zhang_NWPU_task2_1 | ZhangNWPU2025 | transformer, knn, normalizing flow, ensemble | fbank | SMOTE | weighted average | BEATs, EAT | 6 | pre-trained model | ||
67 | Zhang_NWPU_task2_2 | ZhangNWPU2025 | transformer, knn, normalizing flow, ensemble | fbank | SMOTE | weighted average | BEATs, EAT | 6 | pre-trained model | ||
75 | Zhang_NWPU_task2_3 | ZhangNWPU2025 | transformer, knn, normalizing flow, ensemble | fbank | SMOTE | weighted average | BEATs, EAT | 6 | pre-trained model | ||
82 | Zhang_NWPU_task2_4 | ZhangNWPU2025 | transformer, knn, normalizing flow, ensemble | fbank | SMOTE | weighted average | BEATs, EAT | 6 | pre-trained model | ||
118 | Chao_BUCT_task2_1 | ChaoBUCT2025 | CNN | STFT,FFT | mixup | average | zero-padding | ||||
114 | Chao_BUCT_task2_2 | ChaoBUCT2025 | CNN | STFT,FFT | mixup | average | zero-padding | ||||
108 | Chao_BUCT_task2_3 | ChaoBUCT2025 | Attention, CNN, AE | STFT | mixup | ||||||
89 | Li_XJTLU_task2_1 | LiXJTLU2025 | KNN | raw waveform | mixup | average | BEATs | ||||
87 | Li_XJTLU_task2_2 | LiXJTLU2025 | KNN | raw waveform | mixup | average | BEATs | ||||
106 | Li_XJTLU_task2_3 | LiXJTLU2025 | KNN | raw waveform | mixup | average | BEATs | ||||
99 | Li_XJTLU_task2_4 | LiXJTLU2025 | KNN | raw waveform | mixup | average | BEATs | ||||
107 | Wang_ZJU_task2_1 | WangZJU2025 | AE, CNN, BiGRU, BEATs | log-mel energies | |||||||
64 | Wang_ZJU_task2_2 | WangZJU2025 | AE, CNN, BiGRU, BEATs | log-mel energies | |||||||
85 | Wang_ZJU_task2_3 | WangZJU2025 | AE, CNN, BiGRU, BEATs | log-mel energies | |||||||
55 | Wang_ZJU_task2_4 | WangZJU2025 | AE, CNN, BiGRU, BEATs | log-mel energies | |||||||
105 | Lin_IASP_task2_1 | LinIASP2025 | AE | log-mel energies | |||||||
78 | Lin_IASP_task2_2 | LinIASP2025 | AE | log-mel energies | |||||||
74 | Lin_IASP_task2_3 | LinIASP2025 | AE | log-mel energies | |||||||
73 | Lin_IASP_task2_4 | LinIASP2025 | AE | log-mel energies | HTS-AT | pre-trained model | |||||
110 | Lobanov_ITMO_task2_1 | LobanovITMO2025 | AE | spectrogram | SVD | ||||||
98 | Lobanov_ITMO_task2_2 | LobanovITMO2025 | AE | spectrogram | SVD | ||||||
32 | Qian_nivic_task2_1 | Qiannivic2025 | KNN | log-mel energies, spectrogram | mixup | ||||||
28 | Qian_nivic_task2_2 | Qiannivic2025 | KNN | log-mel energies, spectrogram | mixup | ||||||
49 | Qian_nivic_task2_3 | Qiannivic2025 | KNN | log-mel energies, spectrogram | mixup | ||||||
39 | Qian_nivic_task2_4 | Qiannivic2025 | KNN | log-mel energies, spectrogram | mixup | ||||||
16 | Wang_MYPS_task2_1 | WangMYPS2025 | EAT | log-mel energies | |||||||
20 | Wang_MYPS_task2_2 | WangMYPS2025 | EAT | log-mel energies | |||||||
1 | Wang_MYPS_task2_3 | WangMYPS2025 | EAT | log-mel energies | |||||||
4 | Wang_MYPS_task2_4 | WangMYPS2025 | EAT | log-mel energies | |||||||
121 | Emon_HDK_task2_1 | EmonHDK2025 | AE, GRL, Deep SVDD | log-mel energies | Synthetic Anomaly Augmentation, noise bursts, frequency shifts | ||||||
52 | Fu_CUMT_task2_1 | FuCUMT2025 | KNN | log-mel energies | minmum | sound separation | |||||
40 | Fu_CUMT_task2_2 | FuCUMT2025 | KNN | log-mel energies | minmum | sound separation | |||||
41 | Fu_CUMT_task2_3 | FuCUMT2025 | KNN | log-mel energies | minmum | sound separation | |||||
26 | Fu_CUMT_task2_4 | FuCUMT2025 | KNN | log-mel energies | minmum | sound separation | |||||
71 | Ding_HFUU_task2_1 | DingHFUU2025 | VQ-VAE, PixelSNAIL | log-mel energies | average | 2 | |||||
81 | Ding_HFUU_task2_2 | DingHFUU2025 | VQ-VAE, PixelSNAIL | log-mel energies | average | 2 | HPSS | ||||
91 | Ding_HFUU_task2_3 | DingHFUU2025 | AE | log-mel energies | |||||||
59 | Ding_HFUU_task2_4 | DingHFUU2025 | normalizing flow | log-mel energies | mixup | ||||||
3 | Yang_NBU_task2_1 | YangNBU2025 | AE | log-mel energies | |||||||
6 | Yang_NBU_task2_2 | YangNBU2025 | AE | log-mel energies | |||||||
5 | Yang_NBU_task2_3 | YangNBU2025 | AE | log-mel energies | |||||||
8 | Yang_NBU_task2_4 | YangNBU2025 | AE | log-mel energies | |||||||
115 | Kret_CU_task2_1 | KretCU2025 | k-NN | raw waveform | none | HuBERT | pre-trained model | ||||
13 | Zheng_SJTU-AITHU_task2_1 | ZhengSJTU-AITHU2025 | pre-trained models, ensemble | fbank | specaug, add noise | median | 36 | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT | |||
11 | Zheng_SJTU-AITHU_task2_2 | ZhengSJTU-AITHU2025 | pre-trained models, ensemble | STFT, fbank | specaug, add noise | median | 51 | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT | |||
15 | Zheng_SJTU-AITHU_task2_3 | ZhengSJTU-AITHU2025 | pre-trained models, ensemble | STFT, fbank | specaug, add noise | median | 21 | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT | |||
12 | Zheng_SJTU-AITHU_task2_4 | ZhengSJTU-AITHU2025 | pre-trained models, ensemble | STFT, fbank | specaug, add noise | median | 21 | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT | |||
102 | Zhao_CUMT_task2_1 | ZhaoCUMT2025 | MobileFaceNet | log-mel energies | specaug | BEATs | |||||
101 | Zhao_CUMT_task2_2 | ZhaoCUMT2025 | MobileFaceNet | log-mel energies | specaug | BEATs | |||||
61 | Zhao_CUMT_task2_3 | ZhaoCUMT2025 | AENet | log-mel energies | BEATs | ||||||
62 | Zhao_CUMT_task2_4 | ZhaoCUMT2025 | AENet | log-mel energies | BEATs | ||||||
23 | Ozeki_MELCO_task2_1 | OzekiMELCO2025 | Contrastive learning based on the SimSiam framework, kNN | Mel spectrogram | Gain adjustment, Polarity Inversion, Pitch Shifting, Time Stretching, Additive Background Noise | CED | pre-trained model, embeddings, data augmentation | Normalization, Patchification | |||
60 | Ozeki_MELCO_task2_2 | OzekiMELCO2025 | Contrastive learning based on the SimSiam framework, kNN | Mel spectrogram | Gain adjustment, Polarity Inversion, Pitch Shifting, Time Stretching, Additive Background Noise | CED | pre-trained model, embeddings, data augmentation | Normalization, Patchification | |||
69 | Ozeki_MELCO_task2_3 | OzekiMELCO2025 | Contrastive learning based on the SimSiam framework, kNN | Mel spectrogram | Gain adjustment, Polarity Inversion, Pitch Shifting, Time Stretching, Additive Background Noise | CED | pre-trained model, embeddings, data augmentation | Normalization, Patchification | |||
79 | Ozeki_MELCO_task2_4 | OzekiMELCO2025 | Contrastive learning based on the SimSiam framework, kNN | Mel spectrogram | Gain adjustment, Polarity Inversion, Pitch Shifting, Time Stretching, Additive Background Noise | CED | pre-trained model, embeddings, data augmentation | Normalization, Patchification | |||
25 | Huang_XJU_task2_1 | HuangXJU2025 | CNN | log-mel energies, spectrum | mixup, specaug | maximum | BEATs | pre-trained model | |||
27 | Huang_XJU_task2_2 | HuangXJU2025 | CNN | log-mel energies, spectrum | mixup, specaug | maximum | EAT | pre-trained model | |||
35 | Huang_XJU_task2_3 | HuangXJU2025 | CNN | log-mel energies, spectrum | mixup, specaug | maximum | BEATs, EAT | pre-trained model | |||
37 | Huang_XJU_task2_4 | HuangXJU2025 | CNN | log-mel energies, spectrum | mixup, specaug | maximum | BEATs, EAT | pre-trained model | |||
7 | Fujimura_NU_task2_1 | FujimuraNU2025 | CNN | spectrogram, spectrum | mixup | maximum | BEATs, EAT, SSLAM | 32 | pre-trained model, training of enhancement model | DNN-based Enhancement | |
21 | Fujimura_NU_task2_2 | FujimuraNU2025 | CNN | spectrogram, spectrum | mixup | maximum | BEATs, EAT, SSLAM | 19 | pre-trained model, training of enhancement model | DNN-based Enhancement | |
14 | Fujimura_NU_task2_3 | FujimuraNU2025 | CNN | spectrogram, spectrum | mixup | maximum | BEATs, EAT, SSLAM | 51 | pre-trained model, training of enhancement model | DNN-based Enhancement | |
9 | Fujimura_NU_task2_4 | FujimuraNU2025 | CNN | spectrogram, spectrum | mixup | maximum | BEATs, EAT, SSLAM | 67 | pre-trained model, training of enhancement model | DNN-based Enhancement | |
19 | Jiang_THUEE_task2_1 | JiangTHUEE2025 | pre-trained models, ensemble | STFT, fbank | specaug | median | 15 | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT | |||
10 | Jiang_THUEE_task2_2 | JiangTHUEE2025 | pre-trained models, diffusion, ensemble | STFT, fbank | specaug | median | 74 | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT, Tangoflux | |||
18 | Jiang_THUEE_task2_3 | JiangTHUEE2025 | pre-trained models, diffusion, ensemble | STFT, fbank | specaug | median | 34 | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT | |||
17 | Jiang_THUEE_task2_4 | JiangTHUEE2025 | pre-trained models, diffusion, ensemble | STFT, fbank | specaug | median | 38 | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT, Tangoflux | |||
119 | Bian_TGU_task2_1 | BianTGU2025 | MAE | log-mel spectrogram | mixup | ||||||
116 | Bian_TGU_task2_2 | BianTGU2025 | MAE | log-mel spectrogram | mixup | ||||||
104 | Bian_TGU_task2_3 | BianTGU2025 | MAE | log-mel spectrogram | mixup | ||||||
120 | Bian_TGU_task2_4 | BianTGU2025 | MAE | log-mel spectrogram | mixup | ||||||
57 | Sera_TMU_task2_1 | SeraTMU2025 | ArcFace, Multi-task learning | log-mel energies | BEATs | ||||||
113 | Kim_DAU_task2_1 | KimDAU2025 | VAE, Gradient adcent, CNN | STFT | |||||||
111 | Kim_DAU_task2_2 | KimDAU2025 | LDM, AE | Linear Spectrogram | Tango, Audio Flamingo 2 | Tango, Audio Flamingo 2, LAION-CLAP | |||||
34 | Wang_UniS_task2_1 | WangUniS2025 | k-means | log-mel energies | mixup, specaug | BEATs | |||||
46 | Wang_UniS_task2_2 | WangUniS2025 | k-means | log-mel energies | mixup, specaug | BEATs | |||||
53 | Wang_UniS_task2_3 | WangUniS2025 | k-means | log-mel energies | mixup, specaug | Dasheng | |||||
90 | Wang_UniS_task2_4 | WangUniS2025 | k-means | log-mel energies | mixup | ||||||
22 | Guan_HEU_task2_1 | GuanHEU2025 | KNN | log-mel energies | |||||||
48 | Guan_HEU_task2_2 | GuanHEU2025 | KNN | log-mel energies | |||||||
117 | Guan_HEU_task2_3 | GuanHEU2025 | KNN | log-mel energies | |||||||
68 | Guan_HEU_task2_4 | GuanHEU2025 | KNN | log-mel energies | |||||||
31 | Kim_AISTAT_task2_1 | KimAISTAT2025 | k-means | log-mel spectrogram | specaug | weighted average | BEATs, EAT | 6 | pre-training | ||
33 | Kim_AISTAT_task2_2 | KimAISTAT2025 | k-means | log-mel spectrogram | specaug | weighted average | BEATs, EAT | 6 | pre-training | ||
36 | Kim_AISTAT_task2_3 | KimAISTAT2025 | k-means | log-mel spectrogram | specaug | weighted average | BEATs, EAT | 6 | pre-training | ||
29 | Kim_AISTAT_task2_4 | KimAISTAT2025 | k-means | log-mel spectrogram | specaug | weighted average | BEATs, EAT | 6 | pre-training |
Technical reports
Audio DisMAE: Unsupervised Acoustic Anomaly Detection via Disentangled Masked Autoencoder
Yuren Bian, Jiayun Chen
Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China and Tiangong University, Tianjin, China
Bian_TGU_task2_1 Bian_TGU_task2_2 Bian_TGU_task2_3 Bian_TGU_task2_4
Audio DisMAE: Unsupervised Acoustic Anomaly Detection via Disentangled Masked Autoencoder
Yuren Bian, Jiayun Chen
Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin, China and Tiangong University, Tianjin, China
Abstract
This technical report presents our submission to DCASE 2025 Task 2, which addresses unsupervised anomalous sound detection under domain shift conditions. We extend the Disentangled Masked Autoencoder (DisMAE), originally proposed for visual domain generalization, to the audio domain. In our approach, machine sounds are first transformed into log-Mel spectrograms and then fed into the DisMAE framework. The semantic branch is designed to reconstruct domain-invariant features, while the variational branch captures domain-specific attributes such as background noise and device variability. By disentangling these two representations, the model achieves robust reconstruction of normal operating sounds. Reconstruction errors from the primary decoder branch are used as anomaly scores. Experimental results demonstrate that the proposed method achieves promising performance on several machine types in the DCASE 2025 dataset.
System characteristics
Classifier | MAE |
System complexity | 11739140, 158256388 |
Acoustic features | log-mel spectrogram |
Data augmentation | mixup |
NCUT SYSTEM FOR FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION
Xichang Cai, Jiafeng Li, Shenghao Liu
School of Artificial Intelligence and Computer Science, North China University of Technology, Beijing, China and North China University of Technology, Beijing, China
Cai_NCUT_task2_1 Cai_NCUT_task2_2 Cai_NCUT_task2_3
NCUT SYSTEM FOR FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION
Xichang Cai, Jiafeng Li, Shenghao Liu
School of Artificial Intelligence and Computer Science, North China University of Technology, Beijing, China and North China University of Technology, Beijing, China
Abstract
Anomalous sound detection (ASD) plays a crucial role in machine condition monitoring, especially in scenarios where collecting anomalous data is impractical. In this report, we propose a First-Shot Unsupervised Anomalous Sound Detection method that requires only normal sound recordings during training. Our approach leverages multiple pre-trained audio embedding models to extract rich and diverse feature representations from machine sounds. Each embedding is evaluated using a K-Nearest Neighbors (KNN) algorithm to compute anomaly scores without supervision. To further improve detection performance and robustness, we perform model-level score fusion by combining the outputs from different embedding models. Experiments conducted on public datasets demonstrate that our method achieves competitive performance in first-shot and low-resource settings, with strong generalization capabilities across machine types and environments. This framework offers a practical and scalable solution for industrial anomaly detection applications.
System characteristics
Classifier | AE, EAT, ensemble |
System complexity | 269992, 309409295, 394902289 |
Acoustic features | log-mel energies |
System embeddings | EAT, EAT, M2D |
The Anomaly Sound Detection Method Based on the Dual-Path CNN and the Autoencoder
Chao Chen, Peng Wu, Pengqi Wang, and Bo Ma
School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
Chao_BUCT_task2_1 Chao_BUCT_task2_2 Chao_BUCT_task2_3
The Anomaly Sound Detection Method Based on the Dual-Path CNN and the Autoencoder
Chao Chen, Peng Wu, Pengqi Wang, and Bo Ma
School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China
Abstract
This report contains a description of the systems submitted to task 2 “First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring” of the DCASE2025 Challenge. The anomaly detection model based on the attention mechanism and the convolutional networks enhanced autoencoder (ACAE) is proposed. In addition, we introduced the DensitySoftmax and the dynamic topic mixture model (DtMM) into the previous unsupervised model to represent the distance between abnormal samples and normal samples. In experimental evaluations, it is shown that both modifications improve the resulting performance and that the proposed. By introducing domain generalization methods, our model achieved improved metrics on the target domain compared to the baseline model.
System characteristics
Classifier | AE, Attention, CNN |
System complexity | 0.55M, 6087766 |
Acoustic features | FFT, STFT |
Data augmentation | mixup |
Decision making | average |
Front end system | zero-padding |
FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION SYSTEM BASED ON PRE-TRAINED MODEL
Sanghyeok Chung, Sunmook Choi, Seungeun Lee, Kihwan Lee, Il-Youp Kwak, Seungsang Oh
Department of Mathematics, Korea University, Seoul, South Korea and Center for Applied Mathematics, Cornell University, Ithaca, NY, USA and Department of Statistics and Data Science, Chung-Ang University, Seoul, South Korea
CHUNG_KUCAU_task2_1 CHUNG_KUCAU_task2_2 CHUNG_KUCAU_task2_3 CHUNG_KUCAU_task2_4
FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION SYSTEM BASED ON PRE-TRAINED MODEL
Sanghyeok Chung, Sunmook Choi, Seungeun Lee, Kihwan Lee, Il-Youp Kwak, Seungsang Oh
Department of Mathematics, Korea University, Seoul, South Korea and Center for Applied Mathematics, Cornell University, Ithaca, NY, USA and Department of Statistics and Data Science, Chung-Ang University, Seoul, South Korea
Abstract
This technical report presents our approach for Task 2 of the DCASE2025 Challenge, First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring. To tackle the challenge of detecting anomalous sounds, we utilize a pre-trained model as a feature extractor. We further adapt the model to the task using Low-Rank Adaptation (LoRA), allowing efficient fine-tuning. Anomaly scores are then computed using a k-nearest neighbors algorithm on standardized feature vectors. Experimental results on the development set demonstrate that our proposed system significantly outperforms the official baseline, validating the effectiveness of our approach.
System characteristics
Classifier | KNN, pre-trained model |
System complexity | 90M |
Acoustic features | log mel spectrogram |
System embeddings | BEATs |
Front end system | zero padding or truncated to 10 seconds |
Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daisuke Niizumi, Davide Albertini, Roberto Sannino, Simone Pradolini, Filippo Augusti, Keisuke Imoto, Kota Dohi, Harsh Purohit, Takashi Endo, Yohei Kawaguchi
Research and Development Group, Hitachi, Ltd., Tokyo, Japan and NTT Communication Science Labs, Kanagawa, Japan and STMicroelectronics, Italy and Doshisha University, Kyoto, Japan
DCASE2025_baseline_task2_MAHALA DCASE2025_baseline_task2_MSE
Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daisuke Niizumi, Davide Albertini, Roberto Sannino, Simone Pradolini, Filippo Augusti, Keisuke Imoto, Kota Dohi, Harsh Purohit, Takashi Endo, Yohei Kawaguchi
Research and Development Group, Hitachi, Ltd., Tokyo, Japan and NTT Communication Science Labs, Kanagawa, Japan and STMicroelectronics, Italy and Doshisha University, Kyoto, Japan
Abstract
We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge Task 2: "First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring". Continuing from last year's DCASE 2023 Challenge Task 2, we organize the task as a first-shot problem under domain generalization required settings. The main goal of the first-shot problem is to enable rapid deployment of ASD systems for new kinds of machines without the need for machine-specific hyperparameter tunings. This problem setting was realized by (i) giving only one section for each machine type and (ii) having completely different machine types for the development and evaluation datasets. For the DCASE 2024 Challenge Task 2, data of completely new machine types were newly collected and provided as the evaluation dataset. In addition, attribute information such as the machine operation conditions were concealed for several machine types to mimic situations where such information are unavailable. We will add challenge results and analysis of the submissions after the challenge submission deadline.
System characteristics
Classifier | AE |
System complexity | 267928 |
Acoustic features | log-mel energies |
Abnormal Sound Detection Based on Domain Generalization
Junkai Ding, Bo Pang, Xingcheng Zhu, Shengbing Chen, Zhifang Zheng
Research and Development Group, Hefei University, Hefei, China
Ding_HFUU_task2_1 Ding_HFUU_task2_2 Ding_HFUU_task2_3 Ding_HFUU_task2_4
Abnormal Sound Detection Based on Domain Generalization
Junkai Ding, Bo Pang, Xingcheng Zhu, Shengbing Chen, Zhifang Zheng
Research and Development Group, Hefei University, Hefei, China
Abstract
This technical report describes the anomalous sound detection system we submitted for DCASE 2025 Task 2. DCASE Task2 aims to solve the core pain points of machine health monitoring in industrial scenarios. Compared to last year’s task, this year’s additional datasets have been added, and small and difficult to detect domain shifts have been added, but the main focus is still on the unsupervised learning framework to achieve the first detection of unknown abnormal sounds from industrial machines, while overcoming the interference of domain shifts. At present, the pain points of this task are: 1.domain shift sensitivity, 2. finite sample learning bottleneck, and 3. complexity of abnormal patterns. In order to solve these problems, we submit four methods for detecting abnormal sounds in machine status,In the first and second methods, we used the joint model composed of VQ-VAE and PixelSNAIL for anomaly detection, and in the third and fourth methods, we used AE and flow models for anomaly detection. All four methods use feature vectors extracted from convolutional neural networks to identify anomalous sounds through anomaly detection algorithms. Experiments on the development set show that the performance of these four methods is better than that of the benchmark model.
System characteristics
Classifier | AE, PixelSNAIL, VQ-VAE, normalizing flow |
System complexity | 10931697, 269992 |
Acoustic features | log-mel energies |
Data augmentation | mixup |
Decision making | average |
Subsystem count | 2 |
Front end system | HPSS |
A DUAL-STREAM CNN WITH SUB-CLUSTER ADAPTIVE COSINE LOSS FOR ANOMALOUS SOUND DETECTION
H_ M_nh D_ng
Faculty of Information Technology 1, Posts and Telecommunications Institute of Technology, Ha Noi, Viet Nam
Abstract
This report describes our system for the DCASE 2025 Challenge Task 2: "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring" [1]. Our approach is based on a dual-stream Convolutional Neural Network (CNN) architecture designed to extract robust features from raw audio signals. One stream processes frequency characteristics via a Fast Fourier Transform (FFT), while the second stream analyzes time-frequency features from a magnitude spectrogram. To enhance model generalization, we employ two data augmentation techniques: Mixup [2] and SpecAugment [3]. The core of our system is a metric learning approach using the Sub-Cluster AdaCos (SCAdaCos) loss function, inspired by AdaCos [4], to learn highly discriminative embeddings. Anomaly scores are calculated based on the cosine similarity between test sample embeddings and pre-computed class centroids from the training data. Our results on the development set show that the system has a foundational capability for anomaly detection, with performance metrics surpassing the random guess baseline.
System characteristics
Classifier | CNN, Ensemble |
System complexity | 269992 |
Acoustic features | FFT, Magnitude Spectrogram |
Data augmentation | Mixup, SpecAugment, Random Crop/Padding |
Decision making | average |
Subsystem count | 2 |
JOINT DOMAIN-ADVERSARIAL AND CONTRASTIVE LATENT OPTIMIZATION FOR UNSUPERVISED AUDIO ANOMALY DETECTION
Taharim Rahman Anon, Jakaria Islam Emon
Hokkaido Denshikiki Co., Ltd., Sapporo, Hokkaido, Japan
Emon_HDK_task2_1
JOINT DOMAIN-ADVERSARIAL AND CONTRASTIVE LATENT OPTIMIZATION FOR UNSUPERVISED AUDIO ANOMALY DETECTION
Taharim Rahman Anon, Jakaria Islam Emon
Hokkaido Denshikiki Co., Ltd., Sapporo, Hokkaido, Japan
Abstract
This paper presents a unified framework for Unsupervised Anomaly Sound Detection (UASD) that combines Convolutional Autoencoders (CAE) with Domain-Adversarial Neural Networks (DANN) and Deep Support Vector Data Description (Deep SVDD). Our approach addresses the critical challenges of domain shift and first-shot generalization in the DCASE 2025 Task 2 challenge. The proposed architecture employs a CAE to learn compact latent representations while a domain classifier with gradient reversal enforces domain-invariant features. The latent space is simultaneously optimized using Deep SVDD to create a tight hypersphere around normal samples. Unlike traditional reconstruction-based methods, our approach leverages both reconstruction loss and a contrastive SVDD loss that pushes generated pseudo-outliers from the normal data boundary, combined with adversarial domain adaptation. Our system demonstrates superior performance over the DCASE 2025 autoencoder baseline, with achieving a total score of 0.77 (versus baseline 0.65). The domain-adversarial training significantly improves target domain generalization, establishing the efficacy of joint optimization for robust anomaly detection in dynamic acoustic environments.
System characteristics
Classifier | AE, Deep SVDD, GRL |
System complexity | 17040000 |
Acoustic features | log-mel energies |
Data augmentation | Synthetic Anomaly Augmentation, noise bursts, frequency shifts |
ENHANCED UNSUPERVISED ANOMALOUS SOUND DETECTION VIA CONVTASNET-BASED SEPARATION AND CONDITIONAL AUTOENCODING
Chenjun Fu, Ronghuan Zhao, Qiang Wang, Hao Wu, Liang Zou
China University of Mining and Technology, XuZhou,China
Fu_CUMT_task2_1 Fu_CUMT_task2_2 Fu_CUMT_task2_3 Fu_CUMT_task2_4
ENHANCED UNSUPERVISED ANOMALOUS SOUND DETECTION VIA CONVTASNET-BASED SEPARATION AND CONDITIONAL AUTOENCODING
Chenjun Fu, Ronghuan Zhao, Qiang Wang, Hao Wu, Liang Zou
China University of Mining and Technology, XuZhou,China
Abstract
This report outlines our approach to first-shot unsupervised anomalous detection for machine condition monitoring, developed for DCASE 2025 Task 2. Given the constraint of only having normal operational data and the availability of clean target device sounds or background noise, our method focuses on leveraging audio separation and a self-supervised AutoEncoder (AE) for anomaly detection. Key components of our approach include training an audio separation module to extract target sounds for effective denoising and data augmentation, encoding audio features via an AutoEncoder trained solely on normal data, and performing conditional modeling with attribute and domain labels to enhance generalization to unknown domains and complex acoustic environments. Anomalies are detected using a K-Nearest Neighbors (KNN)-based method by measuring the distance between each test sample and its nearest neighbors in the training set; greater distances imply higher anomaly likelihood. Our approach achieved notable performance on the development set, demonstrating is effectiveness. The AUC for the target domain was 64.1% and for the source domain was 60.8%. Addtitionally, the Partial AUC values (p=0.1) for the target and source domain was 55.6 % . These results underscore the robustness and applicability of our methodology
System characteristics
Classifier | KNN |
Acoustic features | log-mel energies |
Decision making | minmum |
Front end system | sound separation |
The NU systems for DCASE 2025 Challenge Task 2
Takuya Fujimura, Ibuki Kuroyanagi, Tomoki Toda
Nagoya University, Nagoya, Japan
Fujimura_NU_task2_1 Fujimura_NU_task2_2 Fujimura_NU_task2_3 Fujimura_NU_task2_4
The NU systems for DCASE 2025 Challenge Task 2
Takuya Fujimura, Ibuki Kuroyanagi, Tomoki Toda
Nagoya University, Nagoya, Japan
Abstract
In this report, we present our anomalous sound detection (ASD) systems developed for DCASE 2025 Challenge Task 2. We propose a cascaded approach that integrates a target signal enhancement (TSE) model with a discriminative ASD system. First, we train the TSE model utilizing supplementary clean machine sounds and noise data. Then, we train the discriminative ASD system using the enhanced machine sounds to improve noise robustness. To further improve detection performance, we incorporate recently proposed techniques into the discriminative ASD system: multi-resolution spectrograms, pre-trained self-supervised learning features, and pseudo-label generation. Our final ensemble system has achieved 64.91% in the official scores calculated as a harmonic mean of the area under the curve (AUC) and partial AUC (p= 0.1) over all machine types and domains in the development set.
System characteristics
Classifier | CNN |
System complexity | 1502700000, 2383200000, 3804300000, 4955100000 |
Acoustic features | spectrogram, spectrum |
Data augmentation | mixup |
Decision making | maximum |
System embeddings | BEATs, EAT, SSLAM |
Subsystem count | 19, 32, 51, 67 |
External data usage | pre-trained model, training of enhancement model |
Front end system | DNN-based Enhancement |
Anomalous Sound Detection Using Pre-trained Model With Statistical Feature Difference Representation
Shiheng Zhang, Feiyang Xiao, Shitong Fan, Qiaoxi Zhu, Wenwu Wang, and Jian Guan
College of Computer Science and Technology, Harbin Engineering University, Harbin, China and Ultimo, Australia and Centre for Vision, Speech and Signal Processing (CVSSP), Guildford, UK and College of Computer Science and Technology, Harbin, China
Guan_HEU_task2_1 Guan_HEU_task2_2 Guan_HEU_task2_3 Guan_HEU_task2_4
Anomalous Sound Detection Using Pre-trained Model With Statistical Feature Difference Representation
Shiheng Zhang, Feiyang Xiao, Shitong Fan, Qiaoxi Zhu, Wenwu Wang, and Jian Guan
College of Computer Science and Technology, Harbin Engineering University, Harbin, China and Ultimo, Australia and Centre for Vision, Speech and Signal Processing (CVSSP), Guildford, UK and College of Computer Science and Technology, Harbin, China
Abstract
This report presents GISP-HEU’s submission for Task 2 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge. The submission utilises pre-trained models for feature extraction to obtain refined audio representations. In addition, a statistical weight is formulated based on the differences in audio features between the test and training samples. This weight is applied during the testing phase to enhance the distinction between normal and anomalous audio. The submission comprises four individual systems. System 1 utilises BEATs alongside the statistical feature difference weighting. System 2 builds on System 1 by incorporating clean and noisy data during training. System 3 employs AnoPatch and uses development data spanning DCASE 2022 to DCASE 2025. Finally, System 4 is an ensemble of the previous three systems.
System characteristics
Classifier | KNN |
System complexity | 300000000 |
Acoustic features | log-mel energies |
XJU System for First-Shot Unsupervised Anomalous Sound Detection
Shun Huang, Liang He
School of Computer Science and Technology, Xinjiang University, Urumqi, China and Department of Electronic Engineering, Tsinghua University, Beijing, China
Huang_XJU_task2_1 Huang_XJU_task2_2 Huang_XJU_task2_3 Huang_XJU_task2_4
XJU System for First-Shot Unsupervised Anomalous Sound Detection
Shun Huang, Liang He
School of Computer Science and Technology, Xinjiang University, Urumqi, China and Department of Electronic Engineering, Tsinghua University, Beijing, China
Abstract
Previous studies have shown that using large-scale audio pre-training models for anomaly sound detection under domain shift scenarios has demonstrated significant promise. In this year’s competition, compared to last year, supplementary sets have been added. Due to our lack of understanding in denoising, this dataset was not utilized throughout the training process. In this technical report, we continue to fine-tune large pre-training models, employing subcenter arcface for training, primarily using the BEATs and EAT models. We trained only on the current development set and additional supplementary sets, achieving a score of 64.46% on the development set.
System characteristics
Classifier | CNN |
System complexity | 172.92M, 176.35M, 87.22M, 91.70M |
Acoustic features | log-mel energies, spectrum |
Data augmentation | mixup, specaug |
Decision making | maximum |
System embeddings | BEATs, BEATs, EAT, EAT |
External data usage | pre-trained model |
THUEE SYSTEM FOR DCASE 2025 ANOMALOUS SOUND DETECTION CHALLENGE
Anbai Jiang, Wenrui Liang, Shi Feng, Yihong Qiu, Yixiang Zhao, Junjie Li, Pingyi Fan, Wei-Qiang Zhang, Cheng Lu, Xie Chen, Yanmin Qian, Jia Liu
Department of Electronic Engineering, Tsinghua University, Beijing, China and Schools of Economy, North China Electric Power University, Beijing, China and Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Jiang_THUEE_task2_1 Jiang_THUEE_task2_2 Jiang_THUEE_task2_3 Jiang_THUEE_task2_4
THUEE SYSTEM FOR DCASE 2025 ANOMALOUS SOUND DETECTION CHALLENGE
Anbai Jiang, Wenrui Liang, Shi Feng, Yihong Qiu, Yixiang Zhao, Junjie Li, Pingyi Fan, Wei-Qiang Zhang, Cheng Lu, Xie Chen, Yanmin Qian, Jia Liu
Department of Electronic Engineering, Tsinghua University, Beijing, China and Schools of Economy, North China Electric Power University, Beijing, China and Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Abstract
This technical report presents the THUEE system for the DCASE 2025 anomalous sound detection (ASD) challenge. Motivated by the success of self-supervised learning (SSL) and generative modeling in various modalities and tasks, we build the system by first adapting multiple SSL pre-trained models for ASD. We find that fine-tuning the model with all six DCASE ASD datasets significantly boosts the ASD performance. To address granularity mismatches in machine attributes, we adopt an adaptive prototype modeling scheme. Furthermore, we leverage powerful diffusion-based audio generation models to synthesize samples under minor working conditions, augmenting the imbalanced training set to mitigate domain gaps between source and target distributions. Finally, we conduct mega ensembling of dozens of single models by Bayesian optimization, achieving substantial performance gains. The best ensemble system reaches 74.29% on the DCASE23 dataset, 70.17% on the DCASE24 dataset and 69.35% on the DCASE25 development set.
System characteristics
Classifier | diffusion, ensemble, pre-trained models |
System complexity | 1.3B, 3.5B, 3B, 7B |
Acoustic features | STFT, fbank |
Data augmentation | specaug |
Decision making | median |
Subsystem count | 15, 34, 38, 74 |
External data usage | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT, Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT, Tangoflux |
AISTAT LAB SYSTEM FOR FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION
Hyun Jun Kim, Min Jun Kim, Hyeon Gyu Bae, Changwon Lim
Applied Statistics, Chung-Ang University, Seoul, Korea
Kim_AISTAT_task2_1 Kim_AISTAT_task2_2 Kim_AISTAT_task2_3 Kim_AISTAT_task2_4
AISTAT LAB SYSTEM FOR FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION
Hyun Jun Kim, Min Jun Kim, Hyeon Gyu Bae, Changwon Lim
Applied Statistics, Chung-Ang University, Seoul, Korea
Abstract
This report addresses the AISTAT team’s submission to First-Shot Unsupervised Anomalous Sound Detection task in DCASE2025 Task 2. Unlike the previous years’ challenges, the available training data spans from the training dataset of 2020 to 2025. To effectively learn from the given data, we adopt a two-stage training strategy consisting of pretraining followed by transfer learning. During the transfer learning stage, pseudo-labeling is applied to data without attribute information to assign approximate labels and enhance model adaptation. Also, ArcFace loss and Center loss are employed together to directly reduce class-intra variance. Additionally, to extract more informative audio representations, we leverage the multi-layer aggregation. Through these techniques, our single best model achieved a harmonic mean of 66.12, while our best ensemble model achieved a harmonic mean of 66.78.
System characteristics
Classifier | k-means |
System complexity | 132800000, 199200000, 66400000 |
Acoustic features | log-mel spectrogram |
Data augmentation | specaug |
Decision making | weighted average |
System embeddings | BEATs, EAT |
Subsystem count | 6 |
External data usage | pre-training |
Metadata-Free Text-to-Audio Normal Synthesis and Latent Gradient Perturbation for Unsupervised Anomalous Sound Detection
JeongSik Kim, JongWoo Sung, HyoenJun Bae, SukHwan Lee
Computer Engineering, Dong-A University, Busan, South Korea and Dong-A University, Busan, South Korea
Kim_DAU_task2_1 Kim_DAU_task2_2
Metadata-Free Text-to-Audio Normal Synthesis and Latent Gradient Perturbation for Unsupervised Anomalous Sound Detection
JeongSik Kim, JongWoo Sung, HyoenJun Bae, SukHwan Lee
Computer Engineering, Dong-A University, Busan, South Korea and Dong-A University, Busan, South Korea
Abstract
This technical report shows a fully metadata-free framework for unsupervised anomalous sound detection that synthesizes both normal and anomalous training examples. First, we generate diverse normal audio clips by training and adapting a pretrained Tango text-to-audio model: we apply LoRA and fine-tune Text Encoder and VAE in Tango, and full tuning UNet using three automated prompt strategies (fixed templates, spectrogram-statistic descriptions, and CLAP-filtered captions). Next, we create realistic anomalous spectrograms by perturbing encoded normal representations with gradient ascent and enforcing their magnitude via truncated projection. These synthetic normal and anomalous samples are then used to train a downstream spectrogram-based detector, yielding marked improvements in detection accuracy. In future work, we will close the gap between synthetic and real distributions and extend our approach to direct anomalous audio generation.
System characteristics
Classifier | AE, CNN, Gradient adcent, LDM, VAE |
System complexity | 1326044274, 568462912 |
Acoustic features | Linear Spectrogram, STFT |
Data augmentation | Tango, Audio Flamingo 2 |
External data usage | Tango, Audio Flamingo 2, LAION-CLAP |
FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION WITH FINE-TUNED HUBERT AND K-NEAREST NEIGHBORS
Meghan Kret
Albert Nerkin School of Engineering, Dept. of Electrical Engineering, The Cooper Union, New York, NY, USA
Abstract
We present a lightweight first-shot anomalous-sound-detection (ASD) system for DCASE 2025 Task 2. The method couples a HuBERT-Base backbone―pre-trained on AudioSet at 16 kHz―with a non-parametric k-nearest-neighbor detector in embedding space. Only normal clips from the development and evaluation “train” partitions are required; no synthetic anomalies are generated. A single forward pass extracts a frame-level feature tensor.
System characteristics
Classifier | k-NN |
Acoustic features | raw waveform |
Data augmentation | none |
System embeddings | HuBERT |
External data usage | pre-trained model |
First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Zongmu Lin, Yihao meng, Yuanhang Qian, Yuankai Zhang, Yujie Zhu, Gongping Huang
International Acoustic Signal Processing Laboratory, Wuhan University, Hubei , China and Wuhan University, Hubei , China
Lin_IASP_task2_1 Lin_IASP_task2_2 Lin_IASP_task2_3 Lin_IASP_task2_4
First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Zongmu Lin, Yihao meng, Yuanhang Qian, Yuankai Zhang, Yujie Zhu, Gongping Huang
International Acoustic Signal Processing Laboratory, Wuhan University, Hubei , China and Wuhan University, Hubei , China
Abstract
Automatic detection of machine anomaly remains challenging for machine learning. Unsupervised models have been widely applied in lots of scenarios successfully. This technical report outlines our solutions to Task 2 of the DCASE2025 challenge.The objective is to detect audio recording containing anomalous machine sounds in a test set, when the training dataset itself does not contain any examples of anomalies. Our approaches are based on transformer auto-encoder model and use pretrained model to extract the multi-scale features.
System characteristics
Classifier | AE |
System complexity | 17149084, 493651 |
Acoustic features | log-mel energies |
System embeddings | HTS-AT |
External data usage | pre-trained model |
SUBMISSION FOR THE DCASE2025 TASK2: ROBUST ANOMALY SOUND DETECTION VIA BSS-AUGMENTED PRE-TRAINED MODEL FINE-TUNING
Haifeng Xu, Yizhou Tan , Shengchen Li
Anhui University of Science and Technology., Huainan, China and Xi’an Jiaotong-Liverpool University, Suzhou, China
Li_XJTLU_task2_1 Li_XJTLU_task2_2 Li_XJTLU_task2_3 Li_XJTLU_task2_4
SUBMISSION FOR THE DCASE2025 TASK2: ROBUST ANOMALY SOUND DETECTION VIA BSS-AUGMENTED PRE-TRAINED MODEL FINE-TUNING
Haifeng Xu, Yizhou Tan , Shengchen Li
Anhui University of Science and Technology., Huainan, China and Xi’an Jiaotong-Liverpool University, Suzhou, China
Abstract
Anomaly Sound Detection (ASD) is crucial for predictive maintenance in industrial settings, yet its performance is often severely constrained by high-intensity, non-stationary background noise. To address this challenge, this paper proposes a robust ASD framework incorporating multi-source data fusion and fine-tuning. Specifically, we fuse machine sounds recorded in factories (containing only normal samples) with easily available clean mechanical sounds or environmental noise data. A pretrained BEATs model serves as the feature extractor. To enhance noise robustness, we innovatively introduce a Blind Source Separation (BSS) decoder as an auxiliary task atop the BEATs encoder. This guides the model in learning feature representations that are resistant to noise interference by minimizing BSS loss. Experiments conducted on the DCASE 2025 Development dataset demonstrate that our method significantly outperforms baseline approaches, achieving AUC values of 79.86% and 71.47% on ToyCar and ToyTrain, respectively. This represents relative improvements of 6.69% and 9.71% over baseline systems, underscoring the efficacy of our proposed framework in acoustic event detection and classification scenarios.
System characteristics
Classifier | KNN |
Acoustic features | raw waveform |
Data augmentation | mixup |
Decision making | average |
System embeddings | BEATs |
SVD DECOMPOSITION WITH AUTOENCODERS FOR DCASE 2025 TASK 2
Vladimir Igoshin, Vsevolod Kleshchenko, Dmitry Chirkov, Mark Mirolyubov, Mihail Petrov, Igor Lobanov
School of Physics and Engineering, ITMO University, Saint Petersburg, Russia
Lobanov_ITMO_task2_1 Lobanov_ITMO_task2_2
SVD DECOMPOSITION WITH AUTOENCODERS FOR DCASE 2025 TASK 2
Vladimir Igoshin, Vsevolod Kleshchenko, Dmitry Chirkov, Mark Mirolyubov, Mihail Petrov, Igor Lobanov
School of Physics and Engineering, ITMO University, Saint Petersburg, Russia
Abstract
In this work, we address the problem of single-channel sound anomaly detection by leveraging Singular Value Decomposition (SVD) as a feature extraction and dimensionality reduction technique. Specifically, we apply SVD across the entire dataset of spectrograms and retain only a limited number of dominant components to represent the input signals in a compact latent space. We evaluate two autoencoder-based models on the reduced representations. First one is a challenge baseline autoencoder trained on the low-dimensional features obtained from SVD. Second is transformer-inspired autoencoder that integrates a convolution layer and an attention mechanism to better capture temporal structures indicative of anomalous behavior.
System characteristics
Classifier | AE |
System complexity | 144568, 25216 |
Acoustic features | spectrogram |
Front end system | SVD |
ANOMALOUS SOUND DETECTION METHOD USING CONTRASTIVE LEARNING
Kosei Ozeki, Takeru Shiraga, Takahiko Masuzaki, Nobuaki Tanaka, and Toshiyuki Kuriyama
Artificial Intelligence R&D Dept., Mitsubishi Electric Corporation, Kanagawa, Japan and Mitsubishi Electric Corporation, Kanagawa, Japan
Ozeki_MELCO_task2_1 Ozeki_MELCO_task2_2 Ozeki_MELCO_task2_3 Ozeki_MELCO_task2_4
ANOMALOUS SOUND DETECTION METHOD USING CONTRASTIVE LEARNING
Kosei Ozeki, Takeru Shiraga, Takahiko Masuzaki, Nobuaki Tanaka, and Toshiyuki Kuriyama
Artificial Intelligence R&D Dept., Mitsubishi Electric Corporation, Kanagawa, Japan and Mitsubishi Electric Corporation, Kanagawa, Japan
Abstract
This paper presents methods for anomalous sound detection for DCASE2025 Task 2. The goal of this contest is to identify whether the sounds emitted from target machines are normal or anomaly. We implemented the following approaches: 1. Anomaly detection using a pre-trained model directly. 2. Fine-tuning the DCASE general model learned in Stage1 for individual machines. 3. Implementing the flow of approach 2 with data augmentation using additional data (clean machine data or noise-only data). 4. Performing sound source separation of operation sounds and noise, followed by implementing the flows of approaches 1 or 2. As a result, our approach achieved higher accuracy compared to the baseline method in the evaluation of the development dataset.
System characteristics
Classifier | Contrastive learning based on the SimSiam framework, kNN |
System complexity | 85253504 |
Acoustic features | Mel spectrogram |
Data augmentation | Gain adjustment, Polarity Inversion, Pitch Shifting, Time Stretching, Additive Background Noise |
System embeddings | CED |
External data usage | pre-trained model, embeddings, data augmentation |
Front end system | Normalization, Patchification |
Anomaly Sound Detection Method Based on Training Attribute Classification Models
Fan Chu, Mengui Qian
National Intelligent Voice Innovation Center, Hefei, China
Qian_nivic_task2_1 Qian_nivic_task2_2 Qian_nivic_task2_3 Qian_nivic_task2_4
Anomaly Sound Detection Method Based on Training Attribute Classification Models
Fan Chu, Mengui Qian
National Intelligent Voice Innovation Center, Hefei, China
Abstract
In this report, we present our solution to the DCASE 2025 Challenge Task 2, focusing on First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring. In this year's challenge, some machines still lack attribute and domain labels, while clean machine sound or background noise are provided. Our approach involves using clustering or feature frequency analysis algorithms to assign artificial labels to samples without attribute labels, then training an attribute classification model together with other machines that have attributes. Additionally, we introduce a data augmentation strategy by mixing clean machine sound with background noise to generate simulated data. Finally, we employ the model's embedding to train a KNN model for obtaining anomaly scores. Our system achieves 63.25% in the harmonic mean of AUC and pAUC (p = 0.1) across all machine types and domains on the development set.
System characteristics
Classifier | KNN |
System complexity | 10M |
Acoustic features | log-mel energies, spectrogram |
Data augmentation | mixup |
GENREP FOR FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION OF DCASE 2025 CHALLENGE
Phurich Saengthong, Takahiro Shinozaki
Information and Communications Engineering, Institute of Science Tokyo, Japan and Institute of Science Tokyo, Japan
Saengthong_SCITOK_task2_1 Saengthong_SCITOK_task2_2 Saengthong_SCITOK_task2_3 Saengthong_SCITOK_task2_4
GENREP FOR FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION OF DCASE 2025 CHALLENGE
Phurich Saengthong, Takahiro Shinozaki
Information and Communications Engineering, Institute of Science Tokyo, Japan and Institute of Science Tokyo, Japan
Abstract
Recent advances in large-scale pre-trained audio models have shown that frozen embeddings can provide robust and transferable representations for general audio tasks. Building on GenRep, which uses frozen embeddings with k-nearest neighbors and domain-wise Z-score normalization for anomaly detection under domain shift, we extend this approach by exploring several directions, including normalization strategies, model scaling, and feature ensembling. First, we study alternative normalization methods such as global Z-score normalization, local density normalization, and domain-wise local density normalization. Second, we evaluate pre-trained audio encoders ranging from 5M to 300M parameters on the DCASE2025 Task 2 dataset to examine the impact of model scale. Third, we study the effect of ensemble fusion using features from multiple frozen encoders. Our results indicate that even the smallest pre-trained encoder (5.49M) can outperform a baseline autoencoder, and that larger models and ensembling contribute to further improvements without updating model parameters. The code is available open-source.
System characteristics
Classifier | ensemble |
System complexity | 5490300, 569284800 |
Acoustic features | log-mel energies |
Decision making | average |
System embeddings | BEATs, M2D-CLAP, EAT, SSLAM, CED |
Subsystem count | 5 |
Distance-Based Unsupervised Anomalous Sound Detection with Attentive Statistics Pooling and ArcFace Multi-Task Learning
Masayuki Sera, Takao Kawamura, Nobutaka Ono
computer science, Tokyo Metropolitan University, Tokyo, Japan and Tokyo Metropolitan University, Tokyo, Japan
Sera_TMU_task2_1
Distance-Based Unsupervised Anomalous Sound Detection with Attentive Statistics Pooling and ArcFace Multi-Task Learning
Masayuki Sera, Takao Kawamura, Nobutaka Ono
computer science, Tokyo Metropolitan University, Tokyo, Japan and Tokyo Metropolitan University, Tokyo, Japan
Abstract
In this technical report, we describe our submission to the DCASE 2025 Challenge Task 2, titled “First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring.” Our system is a distance-based anomalous sound detection method that determines whether a test input is normal or anomalous based on the Euclidean distance to embeddings of normal data. To obtain effective embeddings, we first apply the pretrained acoustic model BEATs to the input audio clip without any fine-tuning. The resulting patch-level features are then aggregated using Attentive Statistics Pooling to form a fixed-dimensional representation. To further improve the embeddings, we employ AcrFace-based multi-task learning with machine type and attribute classification objectives, which are used only during training. Our system achieved an Ω score of 0.6132 on the official development dataset, corresponding to a 5.3 percentage point improvement over the baseline system (0.5599).
System characteristics
Classifier | ArcFace, Multi-task learning |
System complexity | 90,409,441.0 |
Acoustic features | log-mel energies |
System embeddings | BEATs |
Patch-Based Contrastive Learning With Latent Space Clustering For Unsupervised Sound Anomaly Detection
Abhivanth Sivaprakash, K Krish Sundaresh, Ankith Vijayyan, Adhithya Srivatsan, Chandrakala S
Computer Science Engineering, Shiv Nadar University, Chennai, Chennai, India
Vijayyan_SNUC_task2_1
Patch-Based Contrastive Learning With Latent Space Clustering For Unsupervised Sound Anomaly Detection
Abhivanth Sivaprakash, K Krish Sundaresh, Ankith Vijayyan, Adhithya Srivatsan, Chandrakala S
Computer Science Engineering, Shiv Nadar University, Chennai, Chennai, India
Abstract
This report presents our submission for the DCASE 2025 Challenge Task 2 on first-shot unsupervised anomalous sound detection. We propose a contrastive learning-based framework designed to capture fine-grained patterns from spectrogram representations while adapting to both attribute-rich and attribute-absent machine conditions. The method leverages local feature learning and selectively integrates auxiliary metadata to enhance generalization under domain shifts. Training is performed jointly across all machine types using only normal data. Anomaly scoring is carried out in a learned embedding space using a statistical distance-based method. Our approach outperforms official baselines in both source and target domains on the development dataset, demonstrating strong potential for robust and flexible industrial anomaly detection.
System characteristics
Classifier | contrastive learning |
System complexity | 21469992 |
Acoustic features | log-mel energies |
Data augmentation | RandomResizedCrop, RandomHorizontalFlip, ColorJitter, RandomGrayscale |
PRE-TRAINED MODEL ENHANCED ANOMALOUS SOUND DETECTION SYSTEM FOR DCASE2025 TASK2
Lei Wang
individual, individual, Fuyang, China
Wang_MYPS_task2_1 Wang_MYPS_task2_2 Wang_MYPS_task2_3 Wang_MYPS_task2_4
PRE-TRAINED MODEL ENHANCED ANOMALOUS SOUND DETECTION SYSTEM FOR DCASE2025 TASK2
Lei Wang
individual, individual, Fuyang, China
Abstract
This study proposes a robust approach to address DCASE2025 Challenge Task 2, focusing on First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring. The task presents a unique challenge of training models with and without attribute information, necessitating robust performance under both scenarios. To address this challenge, we utilize advanced pre-trained models as the feature extraction backbone, integrate attribute classification and domain classification networks, and fine-tune them on the DCASE2025 Task2 dataset. Finally, we employ a KNN model as the backend for calculating anomaly scores. Benefiting from the powerful feature extraction capability of the pre-trained model, our system achieves a competitive harmonic mean of AUC and PAUC(p = 0.1) of 60.9% on the development set.
System characteristics
Classifier | EAT |
System complexity | 87M |
Acoustic features | log-mel energies |
FINE-TUNING PRE-TRAINED AUDIO MODELS FOR ANOMALOUS SOUND DETECTION
Junjie Wang
Wang_UniS_task2_1 Wang_UniS_task2_2 Wang_UniS_task2_3 Wang_UniS_task2_4
FINE-TUNING PRE-TRAINED AUDIO MODELS FOR ANOMALOUS SOUND DETECTION
Junjie Wang
Abstract
This technical report presents our solution to Task 2 of the DCASE 2025 Challenge, which focuses on unsupervised anomalous sound detection for machine condition monitoring. We developed four subsystems, all of which detect anomalies by extracting embeddings and applying outlier detection algorithms. Among them, three systems utilize fine-tuned audio pre-trained models for embedding extraction, while the remaining one employs a convolutional neural network. Unlike previous approaches that classify machine meta-data, our system enhances domain generalization by training models to distinguish between machine sounds and background noise.
System characteristics
Classifier | k-means |
System complexity | 0.6B, 3M, 90M |
Acoustic features | log-mel energies |
Data augmentation | mixup, mixup, specaug |
System embeddings | BEATs, Dasheng |
FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION BASED ON FUSION OF CNN-AE AND BEATs-KNN
Wang Xiaoliang, Ming Ao
Zhejiang University, Hangzhou, China
Abstract
This technical report presents our submission to the DCASE 2025 Challenge Task 2. We propose a fusion-based system combining a CNN-BiGRU-Attention Autoencoder with a BEATs-KNN model to improve unsupervised anomalous sound detection (ASD). Both models are independently trained and then combined at the score level using a weighted average strategy. The fusion weights are optimized using the development dataset. Results show that this hybrid approach improves the robustness of anomaly detection across multiple machine types. Through the fusion of various models and methods, we have achieved a hmean of 66.00% on the development dataset.
System characteristics
Classifier | AE, BEATs, BiGRU, CNN |
System complexity | 269992 |
Acoustic features | log-mel energies |
First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Based on Ensemble Learning and Domain Generalization
Ting Wu, Lu Han, Zhaoli Yan, Xiaobin Cheng, Jian Wen, Jun Yang
State Key Laboratory of Acoustics and Marine Information, The Institute of Acoustics of the Chinese Academy of Sciences, Beijing, China and The Institute of Acoustics of the Chinese Academy of Sciences, Beijing, China and Beijing University of Chemical Technology, Beijing, China
WT_IACAS_task2_1 WT_IACAS_task2_2 WT_IACAS_task2_3 WT_IACAS_task2_4
First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Based on Ensemble Learning and Domain Generalization
Ting Wu, Lu Han, Zhaoli Yan, Xiaobin Cheng, Jian Wen, Jun Yang
State Key Laboratory of Acoustics and Marine Information, The Institute of Acoustics of the Chinese Academy of Sciences, Beijing, China and The Institute of Acoustics of the Chinese Academy of Sciences, Beijing, China and Beijing University of Chemical Technology, Beijing, China
Abstract
Unsupervised pretrained models have achieved remarkable success across a wide range of applications. In this report, an approach is presented for DCASE 2025 Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring. To address this challenge, an anomaly detection algorithm is proposed, which combines density estimation with cross-domain interpolation to robustly detect anomalies. Additionally, a two-stage pretraining strategy within a teacher-student framework is adopted to enhance audio data representation. A dual-headed network architecture is developed to leverage both labeled and unlabeled loss functions, mitigating the scarcity of labeled data. Finally, to optimize the ensemble of several large-scale models, an adaptive weighted combination perturbation search algorithm is introduced to determine the optimal fusion weights. Collectively, these methods achieve a score of 69.94% on the official development dataset, significantly surpassing the baseline model.
System characteristics
Classifier | ResNet, ensemble |
System complexity | 28592295 |
Acoustic features | log-mel energies |
System embeddings | BEATs, EAT, SSLAM |
A TWO STAGE FUSION ANOMALY DETECTION APPROACH FOR TASK2
Jie Yang
Shanghai, China
Yang_NBU_task2_1 Yang_NBU_task2_2 Yang_NBU_task2_3 Yang_NBU_task2_4
A TWO STAGE FUSION ANOMALY DETECTION APPROACH FOR TASK2
Jie Yang
Shanghai, China
Abstract
This technical report details our approach to addressing Task 2 of the DCASE 2025 Challenge. We propose a two stage fusion adaptive anomaly detection scheme which combine adaptive filtering for denoising and separation and neural network-based classifier. First, traditional signal separation and denoising techniques are employed to preprocess the raw audio signal. This stage focuses on suppressing noise, isolating interfering sound sources, and enhancing the signal-to-noise ratio (SNR) of the target machine sound. For the attribute classification network, we leverage the depth-wise separable convolutions and bottleneck structure of MobileFaceNet to efficiently learn deep discriminative features of anomalous sounds. Finally,an anomaly score is computed based on K-Nearest Neighbors (KNN). The results demonstrate that the proposed method achieves significant performance improvements and ensures robust adaptability under varying data conditions. Furthermore, the fraframework's flexibility in handling different types of input data enhances its applicability in real-world industrial machine monitoring scenarios.
System characteristics
Classifier | AE |
System complexity | 1M |
Acoustic features | log-mel energies |
Enhancing Machine Sound Anomaly Detection via Source Separation and Hybrid SSL Fusion
Yucong Zhang, Zhang Chen, Ming Li
Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems, Duke Kunshan University, Suzhou, China
Zhang_DKU_task2_1 Zhang_DKU_task2_2 Zhang_DKU_task2_3 Zhang_DKU_task2_4
Enhancing Machine Sound Anomaly Detection via Source Separation and Hybrid SSL Fusion
Yucong Zhang, Zhang Chen, Ming Li
Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems, Duke Kunshan University, Suzhou, China
Abstract
This technical report presents our solution for DCASE 2025 Task 2: Anomalous Sound Detection for Machine Condition Monitoring. Our approach integrates BEATs and AudioMAE models through two fusion strategies: 1) score-level ensemble of independently fine-tuned models, and 2) feature-level fusion with unified attentive statistical pooling. Both models employ LoRA-based adaptation on combined historical and current DCASE datasets, enhanced by source separation for clean-referenced machines and universal noise augmentation. The anomaly detection mechanism leverages prototype embeddings generated from KMeans clustering and target samples. Achieving a 66.34% average AUC/pAUC score on the development set, our system demonstrates 10.47% improvement over the baseline, highlighting the effectiveness of hybrid fusion strategies in capturing diverse normal sound patterns.
System characteristics
Classifier | AE, ensemble, transformer |
System complexity | 176.33 M, 176.71 M, 177.31 M, 85.23 M |
Acoustic features | log-mel energies, raw waveform |
Data augmentation | noise augmentation, noise augmentation, spectral augmentation |
Decision making | average |
System embeddings | BEATs |
Subsystem count | 2, 3 |
External data usage | simulation of attribute labels, pre-trained model |
FUSION SYSTEM FOR FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION FOR MACHINE CONDITION MONITORING
Zhe Cao, Jichao Zhang, Xiao-Lei Zhang, Chi Zhang, Xuelong Li
School of Marine Science and Technology, Northwestern Polytechnical University, Xian, China and School of Marine Science and Technology; Institute of Artificial Intelligence (TeleAI), Northwestern Polytechnical University; China Telecom, Xian, China; Shanghai, China and Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China
Zhang_NWPU_task2_1 Zhang_NWPU_task2_2 Zhang_NWPU_task2_3 Zhang_NWPU_task2_4
FUSION SYSTEM FOR FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION FOR MACHINE CONDITION MONITORING
Zhe Cao, Jichao Zhang, Xiao-Lei Zhang, Chi Zhang, Xuelong Li
School of Marine Science and Technology, Northwestern Polytechnical University, Xian, China and School of Marine Science and Technology; Institute of Artificial Intelligence (TeleAI), Northwestern Polytechnical University; China Telecom, Xian, China; Shanghai, China and Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China
Abstract
The DCASE 2025 Challenge Task 2 focuses on first-shot unsupervised anomalous sound detection, where the main challenges include domain shift, generalization issues, and the absence of attribute information. To address these problems, we leverage the newly introduced past-year DECASE Challenge Task 2 datasets and the Audioset for model pre-training to extract audio features. In this work, we employ LoRA fine-tuning, dual-branch feature exchange, and multi-layer feature fusion methods. In addition, data augmentation is utilized to mitigate domain shift, and multiple models are fused to further enhance performance. As a result, an hmean of 67.27% is achieved on the development dataset.
System characteristics
Classifier | ensemble, knn, normalizing flow, transformer |
System complexity | 408,731,805, 408731805 |
Acoustic features | fbank |
Data augmentation | SMOTE |
Decision making | weighted average |
System embeddings | BEATs, EAT |
Subsystem count | 6 |
External data usage | pre-trained model |
Multi-Modal Acoustic Anomaly Detection via Reconstruction and Discriminative Learning with BEATs Representations
Pengyuan Zhao, Zulong Yan, Tianju Zhao, Yutao Zhang, Meng Lei
School of Information and Control Engineering, Xuzhou, CN
Zhao_CUMT_task2_1 Zhao_CUMT_task2_2 Zhao_CUMT_task2_3 Zhao_CUMT_task2_4
Multi-Modal Acoustic Anomaly Detection via Reconstruction and Discriminative Learning with BEATs Representations
Pengyuan Zhao, Zulong Yan, Tianju Zhao, Yutao Zhang, Meng Lei
School of Information and Control Engineering, Xuzhou, CN
Abstract
This technical report focuses on anomalous sound detection (ASD) in DCASE 2025 Task 2, we propose two deep learning approaches based on multimodal feature fusion to enhance robustness and generalization across domains. In the data preparation stage, in order to solve the problem of data complexity, this paper separates the pure sound events and background noise provided by the organizer based on TF-Locoformer, and constructs a more robust data set for model training by reconstructing diversified training samples through random combination. The first approach extracts frame-level waveform features using a fine-tuned BEATs model and aligns them with Mel-spectrogram features extracted by MobileFaceNet. These are fused and passed into an ArcFace classifier for joint attribute and domain classification, enabling discriminative learning and multi-task optimization. The second approach introduces a multimodal autoencoder architecture combining BEATs and TgramNet for hierarchical feature extraction, jointly trained with reconstruction and classification losses. Our best model achieves a pAUC of 0.59.56 on the validation set, demonstrating strong detection performance under multi-source and complex background conditions.highlighting the effectiveness and potential of the proposed methods in real- world, multi-domain ASD scenarios.
System characteristics
Classifier | AENet, MobileFaceNet |
System complexity | 269992, 368432 |
Acoustic features | log-mel energies |
Data augmentation | specaug |
System embeddings | BEATs |
SJTU-AITHU System for DCASE 2025 Anomalous Sound Detection Challenge
Xinhu Zheng, Anbai Jiang, Bing Han, Shuwei Zhang, Wei-Qiang Zhang, Xie Chen, Cheng Lu, Pingyi Fan, Jia Liu, Yanmin Qian
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China and Tsinghua University, Beijing, China and Algorithm Group, Huakong AI Plus Company Limited, Beijing, China and Department of Electronic Engineering, Tsinghua University, Beijing, China and Schools of Economy, North China Electric Power University, Beijing, China
Zheng_SJTU-AITHU_task2_1 Zheng_SJTU-AITHU_task2_2 Zheng_SJTU-AITHU_task2_3 Zheng_SJTU-AITHU_task2_4
SJTU-AITHU System for DCASE 2025 Anomalous Sound Detection Challenge
Xinhu Zheng, Anbai Jiang, Bing Han, Shuwei Zhang, Wei-Qiang Zhang, Xie Chen, Cheng Lu, Pingyi Fan, Jia Liu, Yanmin Qian
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China and Tsinghua University, Beijing, China and Algorithm Group, Huakong AI Plus Company Limited, Beijing, China and Department of Electronic Engineering, Tsinghua University, Beijing, China and Schools of Economy, North China Electric Power University, Beijing, China
Abstract
This report presents our solutions for DCASE 2025 Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring. In this domain, pre-trained models have demonstrated considerable potential, particularly in handling domain shifts. We develop our systems based on BEATs and the EAT family and explore various training strategies to enhance performance. Sub-center loss and noise-aware training are employed to improve system performance. By fusing various models and methods, we achieve an hmean of 69.12% on the development dataset.
System characteristics
Classifier | ensemble, pre-trained models |
System complexity | 2B, 3B, 4.6B |
Acoustic features | STFT, fbank |
Data augmentation | specaug, add noise |
Decision making | median |
Subsystem count | 21, 36, 51 |
External data usage | Audioset, Freesound, MTG-Jamendo, Music4all, BEATs, EAT |
ENHANCED ANOMALY DETECTION APPROACH FOR DCASE 2025 TASK 2
Guirui Zhong, Qing Wang, Jun Du
University of Science and Technology of China, Hefei, China
Zhong_USTC_task2_1 Zhong_USTC_task2_2 Zhong_USTC_task2_3 Zhong_USTC_task2_4
ENHANCED ANOMALY DETECTION APPROACH FOR DCASE 2025 TASK 2
Guirui Zhong, Qing Wang, Jun Du
University of Science and Technology of China, Hefei, China
Abstract
Addressing the unique challenge of the DCASE 2025 Task 2, where the availability of clean machine and noise-only data varies and datasets in previous years are introduced, we propose an enhanced anomaly detection approach that combines data augmentation and two-stage pre-training methods using pre-trained audio separation and self-supervised learning (SSL) models, respectively. Leveraging audio separation models guided by clean machine or noise-only data, our system can separate clean data from noisy data and generate more diverse data in the training phase. Using a lot of machine sound data for two-stage pre-training, the system can better adapt to anomalous sound detection (ASD) task in the downstream fine-tuning task. By integrating these approaches, our system achieves a better performance across different machines on the DCASE 2025 ASD development dataset, ensuring reliable anomaly detection in machine condition monitoring applications.
System characteristics
Classifier | KNN |
System complexity | 87M |
Acoustic features | log-mel energies |
Data augmentation | mixup, addnoise |
Decision making | average |
Subsystem count | 2 |
External data usage | embeddings, pre-trained model |
Front end system | sound separation |
MACHINE ANOMALOUS SOUND DETECTION COMBINING CONVOLUTIONAL AUTO-ENCODER AND CONTRASTIVE LEARNING
Qing Zhou, Sai Wu
College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, China and Xi'an University of Architecture and Technology, Xi'an, China
Zhou_XAUAT_task2_1 Zhou_XAUAT_task2_2 Zhou_XAUAT_task2_3 Zhou_XAUAT_task2_4
MACHINE ANOMALOUS SOUND DETECTION COMBINING CONVOLUTIONAL AUTO-ENCODER AND CONTRASTIVE LEARNING
Qing Zhou, Sai Wu
College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, China and Xi'an University of Architecture and Technology, Xi'an, China
Abstract
Machine anomalous sound detection (MASD) under noisy industrial conditions remains challenging due to limited anomalous samples, background noise interference, and domain shift. This paper proposes a multi-task learning framework combining a semi-supervised convolutional auto-encoder (CAE) with self-supervised classification and contrastive learning to address these issues. The core architecture uses a CAE backbone and the encoder output is projected into an audio embedding vector which is later fed into a linear classifier for self-supervised attribute classification (e.g., domain, operational parameters). Crucially, the framework leverages newly available clean machine data and noise-only data through a contrastive loss term. This loss pulls embeddings of noisy and clean machine samples of the same class closer while pushing those of noisy machine samples away from pure noise samples, enhancing noise robustness. The model is optimized jointly with a combined loss function integrating reconstruction, classification, and contrastive objectives. During inference, reconstruction errors and audio embeddings are concatenated as input features for a domain-aware anomaly detector. Evaluated on the DCASE2025 Task 2 dataset, the proposed method achieves a harmonic mean score of 63.80%, significantly outperforming the baseline. Ablation studies confirm each component’s contribution, demonstrating the effectiveness of the multi-task strategy in learning discriminative and noise-invariant representations for MASD.
System characteristics
Classifier | AE, KNN, LOF |
System complexity | 194881 |
Acoustic features | log-mel energies |
Data augmentation | mixup, specaug, mixup, specaug, smote |
AN EFFICIENCE ANOMALOUS SOUND DETECTION SYSTEM FOR DCASE 2025 TASK 2
Wentao Zhou, Ying Hu, Xin Fan, Nannan Teng, Tianqing Zhou, Fangxu Chen, Qingjing Wan, Qiong Wu, Qin Yang
Key Laboratory of Signal Detection and Processing in Xinjiang, XinJiang University, Urumqi, China and XinJiang University, Urumqi, China
Zhou_XJU_task2_1 Zhou_XJU_task2_2 Zhou_XJU_task2_3 Zhou_XJU_task2_4
AN EFFICIENCE ANOMALOUS SOUND DETECTION SYSTEM FOR DCASE 2025 TASK 2
Wentao Zhou, Ying Hu, Xin Fan, Nannan Teng, Tianqing Zhou, Fangxu Chen, Qingjing Wan, Qiong Wu, Qin Yang
Key Laboratory of Signal Detection and Processing in Xinjiang, XinJiang University, Urumqi, China and XinJiang University, Urumqi, China
Abstract
This technical report describes the system we submitted to DCASE 2025 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring.This year’s tasks are fundamentally aligned with those of last year. To reflect practical application scenarios, machine attributes are not always fully known. Building upon this, additional clean machine data or noise-only data have been incorporated into the training set. Our system employs the pre-trained model BEATs, utilizing the LoRA finetuning approach for the anomalous sound detection task. Arcface loss is incorporated to constrain machines with unknown attributes. Our best system achieved a harmonic mean of 77.13% in the harmonic mean of AUC in the source domain, 56.07% in AUC in the target domain, and 57.72% in pAUC(p=0.1) on the development set.
System characteristics
Classifier | Arcface, KNN |
System complexity | 90 M |
Acoustic features | log-mel spectrogram |
Data augmentation | specaug |
System embeddings | BEATs |
External data usage | BEATs |