Submission


Instructions

The submission deadline is June 10th 23:59 Anywhere on Earth (AoE)

Introduction

Challenge submission consists in submission package (one zip-package) containing system outputs, system meta information, and technical report (pdf file). The technical report can be the same as your DCASE2019 Workshop submission, but please use the template provided for each.

Submission process shortly:

  1. Participants run their system with evaluation dataset, and produce the system output in specified format. Participants are allowed to submit 4 different system outputs per task or subtask (except in Task2, where participants are allowed to submit 3).
  2. Participants create a meta information file to go along the system output to describe the system used to produce this particular output. Meta information file has a predefined format to help the automatic handling of the challenge submissions. Information provided in the meta file will be later used to produce challenge results.
  3. Participants describe their system in a technical report in sufficient detail. There is a template provided for the technical report.
  4. Participants prepare the submission package (zip-file). The submission package contains system outputs, maximum 4 per task, systems meta information and the technical report.
  5. Participants submit the submission package and the technical report to DCASE2019 Challenge.

Please read carefully the requirements for the files included in the submission package!

Submission system

The submission system is now available:

Submission system

  • Create user account and login
  • Go to "All Conferences" tab in the system and type DCASE to filter the list
  • Select "2019 Challenge on Detection and Classification of Acoustic Scenes and Events"
  • Create a new submission

The challenge deadline is 10 June 2019 (AOE).

The technical report in the submission package must contain at least title, authors, and abstract. An updated camera-ready version of the technical report can be submitted separately until 17 June 2019 (AOE).

Note: the submission system does not any send confirmation email. You can check that your submission has been taken into account in your author console. A confirmation email will be sent to all participants once the submissions are closed.

Submission package

Participants are instructed to pack their system output(s), system meta information, and technical report into one zip-package. Example package:


Please prepare your submission zip-file as the provided example. Follow the same file structure and fill meta information with similar structure as the one in *.meta.yaml -files. The zip-file should contain system outputs for all tasks/subtasks, maximum 4 submissions per task/subtask, separate meta information for each system, and technical report(s) covering all submitted systems.

If you submit similar systems for multiple tasks, you can describe everything in one technical report. If your approaches for different tasks are significantly different, prepare one technical report for each and include it in the corresponding task folder.

More detailed instructions for constructing the package can be found in the following sections. Technical report template is available here.

Submission label

Submission label is used to index all your submissions (systems per tasks). To avoid overlapping labels among all submitted systems, use following way to form your label:

[Last name of corresponding author]_[Abbreviation of institute of the corresponding author]_task[task number][subtask letter (optional)]_[index number of your submission (1-4)]

For example the baseline systems would have the following labels:

  • Heittola_TAU_task1a_1
  • Fonseca_UPF_task2_1
  • Adavanne_TAU_task3_1
  • Serizel_ULO_task4_1
  • Cartwright_NYU_task5_1

Package structure

Make sure your zip-package follows provided file naming convention and directory structure:

Zip-package root

└───task1                                           Task1 submissions
│   │   Heittola_TAU_task1.technical_report.pdf     Technical report covering all subtasks
│   │   Heittola_TAU_task1a.technical_report.pdf    (optional) Technical report for subtask A system only
│   │   Heittola_TAU_task1b.technical_report.pdf    (optional) Technical report for subtask B system only
│   │
│   └───Heittola_TAU_task1a_1                       Subtask A System 1 submission files
│   │       Heittola_TAU_task1a_1.meta.yaml         Subtask A System 1 meta information
│   │       Heittola_TAU_task1a_1.output.csv        Subtask A System 1 output
│   :
│   └───Heittola_TAU_task1a_4                       Subtask A System 4 submission files
│   │       Heittola_TAU_task1a_2.meta.yaml         Subtask A System 4 meta information
│   │       Heittola_TAU_task1a_2.output.csv        Subtask A System 4 output
│   │            
│   └───Heittola_TAU_task1b_1                       Subtask B System 1 submission files
│   │       Heittola_TAU_task1b_1.meta.yaml         Subtask B System 1 meta information
│   │       Heittola_TAU_task1b_1.output.csv        Subtask B System 1 output
│   :
│   └───Heittola_TAU_task1b_4                       Subtask B System 4 submission files
│   │       Heittola_TAU_task1b_1.meta.yaml         Subtask B System 4 meta information
│   │       Heittola_TAU_task1b_1.output.csv        Subtask B System 4 output
│   │      
│   └───Heittola_TAU_task1c_4                       Subtask C System 1 submission files
│   │       Heittola_TAU_task1c_1.meta.yaml         Subtask C System 1 meta information
│   │       Heittola_TAU_task1c_1.output.csv        Subtask C System 1 output
│   :            
│   └───Heittola_TAU_task1c_4                       Subtask C System 4 submission files
│           Heittola_TAU_task1c_4.meta.yaml         Subtask B System 4 meta information
│           Heittola_TAU_task1c_4.output.csv        Subtask B System 4 output

└───task2                                           Task2 submissions
│   │   Fonseca_UPF_task2.technical_report.pdf      Technical report                       
│   │
│   └───Fonseca_UPF_task2_1                         System 1 submission files
│   │     Fonseca_UPF_task2_1.meta.yaml             System 1 meta information
│   │     Fonseca_UPF_task2_1.output.csv            System 1 output   
│   :
│   │
│   └───Fonseca_UPF_task2_3                         System 3 submission files
│         Fonseca_UPF_task2_3.meta.yaml             System 3 meta information
│         Fonseca_UPF_task2_3.output.csv            System 3 output   

└───task3                                           Task3 submissions
│   │   Adavanne_TAU_task3.technical_report.pdf     Technical report
│   │
│   └───Adavanne_TAU_task3_1                        System 1 submission files
│   │     Adavanne_TAU_task3_1.meta.yaml            System 1 meta information
│   │     Adavanne_TAU_task3_1                      System 1 output files both development and evaluation (500 files in total)
│   :
│   │
│   └───Adavanne_TAU_task3_4                        System 4 submission files
│         Adavanne_TAU_task3_2.meta.yaml            System 4 meta information
│         Adavanne_TAU_task3_2                      System 4 output files both development and evaluation (500 files in total)

└───task4                                           Task4 submissions
│   │   Serizel_ULO_task4.technical_report.pdf      Technical report                      
│   │
│   └───Serizel_ULO_task4_1                         System 1 submission files
│   │     Serizel_ULO_task4_1.meta.yaml             System 1 meta information
│   │     Serizel_ULO_task4_1.output.csv            System 1 output
│   :
│   │
│   └───Serizel_ULO_task4_4                         System 4 submission files
│         Serizel_ULO_task4_4.meta.yaml             System 4 meta information
│         Serizel_ULO_task4_4.output.csv            System 4 output

└───task5                                           Task5 submissions
    │   Cartwright_NYU_task5.technical_report.pdf   Technical report

    └───Cartwright_NYU_task5_1                      System 1 submission files
    │     Cartwright_NYU_task5_1.meta.yaml          System 1 meta information
    │     Cartwright_NYU_task5_1.output.csv         System 1 output
    :

    └───Cartwright_NYU_task5_4                      System 4 submission files
          Cartwright_NYU_task5_2.meta.yaml          System 4 meta information
          Cartwright_NYU_task5_2.output.csv         System 4 output

System outputs

Participants must submit the results for the provided evaluation datasets.

  • Follow the system output format specified in the task description.

  • Tasks are independent. You can participate to a single task or multiple tasks.

  • Multiple submissions for the same task are allowed (maximum 4 per task, except in Task2 where only 3 are allowed). Use a running index in the submission label, and give more detailed names for the submitted systems in the system meta information files. Please mark carefully the connection between the submitted systems and system parameters description in the technical report (for example by referring to the systems by using the submission label or system name given in the system meta information file).

  • Submitted system outputs will be published online on the DCASE2019 website later to allow future evaluations.

Technical report

All participants are expected to submit a technical report about the submitted system, to help the DCASE community better understand how the algorithm works.

Technical reports are not peer-reviewed. The technical reports will be published on the challenge website together with all other information about the submitted system. For the technical report it is not necessary to follow closely the scientific publication structure (for example there is no need for extensive literature review). The report should however contain sufficient description of the system.

Please report the system performance using the provided cross-validation setup or development set, according to the task. For participants taking part in multiple tasks, one technical report covering all tasks is sufficient, if the systems have only small differences. Describe the task specific parameters in the report.

Participants can also submit the same report as a scientific paper to DCASE 2019 Workshop. In this case, the paper must respect the structure of a scientific publication, and be prepared according to the provided Workshop paper instructions and template. Please note that the template is slightly different, and you will have to create a separate submission to the DCASE2019 Workshop track in the submission system. Please refer to the workshop webpage for more details. DCASE2019 Workshop papers will be peer-reviewed.

Template

Reports are in format 4+1 pages. Papers are maximum 5 pages, including all text, figures, and references, with the 5th page containing only references. The templates for technical report are available here:

Latex template (275 KB)
version 1.0 (.zip)


Word template (38 KB)
version 1.0 (.docx)



Meta information

In order to allow meta analysis of submitted systems, participants should provide rough meta information presented in a structured and correctly formatted YAML-file.

See example meta files below for each baseline system. These examples are also available in the example submission package. Meta file structure is mostly the same for all tasks, only the metrics collected in results->development_dataset-section differ per challenge task.

Example meta information file for Task 1 baseline system task1/Heittola_TAU_task1a_1/Heittola_TAU_task1a_1.meta.yaml:

# Submission information
submission:
  # Submission label
  # Label is used to index submissions, to avoid overlapping codes among submissions
  # use following way to form your label:
  # [Last name of corresponding author]_[Abbreviation of institute of the corresponding author]_task[task number]_[index number of your submission (1-4)]
  label: Heittola_TAU_task1a_1

  # Submission name
  # This name will be used in the results tables when space permits
  name: DCASE2019 baseline system

  # Submission name abbreviated
  # This abbreviated name will be used in the results table when space is tight, maximum 10 characters
  abbreviation: Baseline

  # Submission authors in order, mark one of the authors as corresponding author.
  authors:
    # First author
    - lastname: Heittola
      firstname: Toni
      email: toni.heittola@tuni.fi                     # Contact email address
      corresponding: true                              # Mark true for one of the authors

      # Affiliation information for the author
      affiliation:
        abbreviation: TAU
        institute: Tampere University
        department: Computing Sciences
        location: Tampere, Finland

    # Second author
    - lastname: Mesaros
      firstname: Annamaria
      email: annamaria.mesaros@tuni.fi                 # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: TAU
        institute: Tampere University
        department: Computing Sciences
        location: Tampere, Finland

# System information
system:
  # System description, meta data provided here will be used to do
  # meta analysis of the submitted system. Use general level tags, if possible use the tags provided in comments.
  # If information field is not applicable to the system, use "!!null".
  description:

    # Audio input
    input_channels: mono                  # e.g. one or multiple [mono, binaural, left, right, mixed, ...]
    input_sampling_rate: 48kHz            #

    # Acoustic representation
    acoustic_features: log-mel energies   # e.g one or multiple [MFCC, log-mel energies, spectrogram, CQT, ...]

    # Data augmentation methods
    data_augmentation: !!null             # [time stretching, block mixing, pitch shifting, ...]

    # Machine learning
    # In case using ensemble methods, please specify all methods used (comma separated list).
    machine_learning_method: CNN          # e.g one or multiple [GMM, HMM, SVM, kNN, MLP, CNN, RNN, CRNN, NMF, random forest, ensemble, ...]

    # Ensemble method subsystem count
    # In case ensemble method is not used, mark !!null.
    ensemble_method_subsystem_count: !!null # [2, 3, 4, 5, ... ]

    # Decision making methods
    decision_making: !!null               # [majority vote, ...]

    # External data usage method
    external_data_usage: !!null           # [directly, embeddings, pre-trained model, ...]

  # System complexity, meta data provided here will be used to evaluate
  # submitted systems from the computational load perspective.
  complexity:

    # Total amount of parameters used in the acoustic model. For neural networks, this
    # information is usually given before training process in the network summary.
    # For other than neural networks, if parameter count information is not directly available,
    # try estimating the count as accurately as possible.
    # In case of ensemble approaches, add up parameters for all subsystems.
    # Use numerical value.
    total_parameters: 116118

  # List of external datasets used in the submission. Development dataset is used here as example, list only external datasets
  external_datasets:
    # Dataset name
    - name: TAU Urban Acoustic Scenes 2019, Development dataset

      # Dataset access url
      url: https://doi.org/10.5281/zenodo.2589280

      # Total audio length in minutes
      total_audio_length: 2400            # minutes

  # URL to the source code of the system [optional]
  source_code: https://github.com/toni-heittola/dcase2019_task1_baseline

# System results
results:
  # Full results are not mandatory, but for through analysis of the challenge submissions recommended.
  # If you cannot provide all results, also incomplete results can be reported.

  development_dataset:
    # System result for development dataset with provided the cross-validation setup.

    # Overall accuracy (mean of class-wise accuracies)
    overall:
      accuracy: 62.5

    # Class-wise accuracies
    class_wise:
      airport:
        accuracy: 48.4
      bus:
        accuracy: 62.3
      metro:
        accuracy: 65.1
      metro_station:
        accuracy: 54.5
      park:
        accuracy: 83.1
      public_square:
        accuracy: 40.7
      shopping_mall:
        accuracy: 59.4
      street_pedestrian:
        accuracy: 60.9
      street_traffic:
        accuracy: 86.7
      tram:
        accuracy: 64.0

  public_leaderboard:
    # Team name used in public leaderboard (https://www.kaggle.com/c/dcase2019-task1a-leaderboard)
    team_name: DCASE2019 Task1A baseline

    # System score from public leaderboard (https://www.kaggle.com/c/dcase2019-task1a-leaderboard)
    overall:
      accuracy: 64.3
                

Example meta information file for Task 2 baseline system task2/Fonseca_UPF_task2_1/Fonseca_UPF_task2_1.meta.yaml:

# Submission information
submission:
  # Submission label
  # Label is used to index submissions, to avoid overlapping codes among submissions
  # use following way to form your label:
  # [Last name of corresponding author]_[Abbreviation of institute of the corresponding author]_task[task number]_[index number of your submission (1-4)]
  label: Fonseca_UPF_task2_1

  # Submission name
  # This name will be used in the results tables when space permits
  name: DCASE2018 baseline system

  # Submission name abbreviated
  # This abbreviated name will be used in the results table when space is tight, maximum 10 characters
  abbreviation: Baseline

  # Kaggle Team Name as in Leaderboard (this is important to match the DCASE submission to the Kaggle team)
  kaggle_team_name: my_team_name_as_in_leaderboard

  # Kaggle submission ID
  # Two submissions are eligible for the final private leaderboard, and one additional submission for Judges Award, hence three in total
  # There must be one different *.yaml file per submission, hence three yaml files in total (if you submit to the Judges Award)
  kaggle_submission_ID: 1234567

  # Submission authors in order, mark one of the authors as corresponding author.
  authors:
    # First author
    - lastname: Fonseca
      firstname: Eduardo
      email: eduardo.fonseca@upf.edu                 # Contact email address
      corresponding: true                         # Mark true for one of the authors

      # Affiliation information for the author
      affiliation:
        abbreviation: UPF
        institute: Universitat Pompeu Fabra, Barcelona
        department: Music Technology Group
        location: Barcelona, Spain

    # Second author
    - lastname: Font
      firstname: Frederic
      email: frederic.font@upf.edu                  # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: UPF
        institute: Universitat Pompeu Fabra, Barcelona
        department: Music Technology Group
        location: Barcelona, Spain

    # Third author
    - lastname: Plakal
      firstname: Manoj
      email: plakal@google.com                # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: GOOGLE
        institute: Google Research
        department: Machine Perception Team
        location: New York, USA

    # Fourth author
    - lastname: Ellis
      firstname: Daniel P. W.
      email: dpwe@google.com               # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: GOOGLE
        institute: Google Research
        department: Machine Perception Team
        location: New York, USA


# System information
system:
  # System description, meta data provided here will be used to do
  # meta analysis of the submitted system. Use general level tags, if possible use the tags provided in comments.
  # If information field is not applicable to the system, use "!!null".
  description:

    # Audio input
    input_channels: mono                  # e.g. one or multiple [mono, binaural, left, right, mixed, ...]
    input_sampling_rate: 44.1kHz            #

    # Acoustic representation
    acoustic_features: log-mel energies   # e.g one or multiple [MFCC, log-mel energies, spectrogram, CQT, ...]

    # Data augmentation methods
    data_augmentation: !!null             # [time stretching, block mixing, pitch shifting, ...]

    # Machine learning
    # In case using ensemble methods, please specify all methods used (comma separated list).
    machine_learning_method: CNN          # e.g one or multiple [GMM, HMM, SVM, kNN, MLP, CNN, RNN, CRNN, NMF, random forest, ensemble, ...]

    # Ensemble method subsystem count
    # In case ensemble method is not used, mark !!null.
    ensemble_method_subsystem_count: !!null # [2, 3, 4, 5, ... ]

    # Decision making methods
    decision_making: arithmetic mean      # [majority vote, arithmetic mean, geometric mean,...]

    # Approach used to exploit noisy subset of the train set
    # In case the noisy set is not used, mark !!null.
    noisy_subset: using provided labels    # e.g one or multiple [using provided labels, automatic re-labeling, unsupervised, manual re-labeling, ...]

  # System complexity, meta data provided here will be used to evaluate
  # submitted systems from the computational load perspective.
  complexity:

    # Total amount of parameters used in the acoustic model. For neural networks, this
    # information is usually given before training process in the network summary.
    # For other than neural networks, if parameter count information is not directly available,
    # try estimating the count as accurately as possible.
    # In case of ensemble approaches, add up parameters for all subsystems.
    total_parameters: 3.3M

    # Approximate training time followed by the hardware used
    trainining_time: 8h (1 x Tesla V-100)

  # URL to the source code of the system [optional]
  source_code: https://github.com/DCASE-REPO/dcase2019_task2_baseline

# System results
results:

  public_leaderboard:
    # System score from public leaderboard (https://www.kaggle.com/c/freesound-audio-tagging-2019/leaderboard)
    overall:
      lwlrap: 0.537
                

Example meta information file for Task 3 baseline system task3/Adavanne_TAU_task3_1/Adavanne_TAU_task3_1.meta.yaml:

# Submission information
submission:
  # Submission label
  # Label is used to index submissions, to avoid overlapping codes among submissions
  # use following way to form your label:
  # [Last name of corresponding author]_[Abbreviation of institute of the corresponding author]_task[task number]_[index number of your submission (1-4)]
  label: Adavanne_TAU_task3_1

  # Submission name
  # This name will be used in the results tables when space permits
  name: DCASE2019 Ambisonic example

  # Submission name abbreviated
  # This abbreviated name will be used in the results table when space is tight, maximum 10 characters
  abbreviation: Ambisonic

  # Submission authors in order, mark one of the authors as corresponding author.
  authors:
    # First author
    - lastname: Adavanne
      firstname: Sharath
      email: sharath.adavanne@tuni.fi                   # Contact email address
      corresponding: true                             # Mark true for one of the authors

      # Affiliation information for the author
      affiliation:
        abbreviation: TAU
        institute: Tampere University
        department: Audio Research Group
        location: Tampere, Finland

    # Second author
    - lastname: Politis
      firstname: Archontis
      email: archontis.politis@tuni.fi                   # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: TAU
        institute: Tampere University
        department: Audio Research Group
        location: Tampere, Finland

    # Third author
    - lastname: Virtanen
      firstname: Tuomas
      email: tuomas.virtanen@tuni.fi                   # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: TAU
        institute: Tampere University
        department: Audio Research Group
        location: Tampere, Finland



# System information
system:
  # System description, meta data provided here will be used to do
  # meta analysis of the submitted system. Use general level tags, if possible use the tags provided in comments.
  # If information field is not applicable to the system, use "!!null".
  description:

    # Audio input
    input_format: Ambisonic                  # e.g. Ambisonic or Microphone Array or both
    input_sampling_rate: 48kHz          #

    # Acoustic representation
    acoustic_features: phase and magnitude spectrogram   # e.g one or multiple [phase and magnitude spectrogram, GCC, TDOA ...]

    # Data augmentation methods
    data_augmentation: !!null             # [time stretching, block mixing, pitch shifting, ...]

    # Machine learning
    # In case using ensemble methods, please specify all methods used (comma separated list).
    machine_learning_method: CRNN          # e.g one or multiple [GMM, HMM, SVM, kNN, MLP, CNN, RNN, CRNN, NMF, random forest, ensemble, ...]


  # System complexity, meta data provided here will be used to evaluate
  # submitted systems from the computational load perspective.
  complexity:

    # Total amount of parameters used in the acoustic model. For neural networks, this
    # information is usually given before training process in the network summary.
    # For other than neural networks, if parameter count information is not directly available,
    # try estimating the count as accurately as possible.
    # In case of ensemble approaches, add up parameters for all subsystems.
    total_parameters: 116118

  # URL to the source code of the system [optional]
  source_code: https://github.com/sharathadavanne/seld-dcase2019

# System results
results:

  development_dataset:
    # System result for development dataset with the provided cross-validation setup.

    # Overall score
    overall:
      error_rate: 0.34
      f_score: 79.9
      doa_error: 28.5
      frame_recall: 85.4
                

Example meta information file for Task 4 baseline system task4/Serizel_ULO_task4_1/Serizel_ULO_task4_1.meta.yaml:

# Submission information
submission:
  # Submission label
  # Label is used to index submissions, to avoid overlapping codes among submissions
  # use following way to form your label:
  # [Last name of corresponding author]_[Abbreviation of institute of the corresponding author]_task[task number]_[index number of your submission (1-4)]
  label: Serizel_UL_task4_1

  # Submission name
  # This name will be used in the results tables when space permits
  name: DCASE2019 baseline system

  # Submission name abbreviated
  # This abbreviated name will be used in the results table when space is tight, maximum 10 characters
  abbreviation: Baseline

  # Submission authors in order, mark one of the authors as corresponding author.
  authors:
    # First author
    - lastname: Serizel
      firstname: Romain
      email: romain.serizel@loria.fr                  # Contact email address
      corresponding: true                             # Mark true for one of the authors

      # Affiliation information for the author
      affiliation:
        abbreviation: ULO
        institute: University of Lorraine, Loria
        department: Department of Natural Language Processing & Knowledge Discovery
        location: Nancy, France

    # Second author
    - lastname: Turpault
      firstname: Nicolas
      email: nicolas.turpault@inria.fr                # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: INR
        institute: Inria Nancy Grand-Est
        department: Department of Natural Language Processing & Knowledge Discovery
        location: Nancy, France

    - lastname: Salamon
      firstname: Justin
      email: justin.salamon@nyu.edu                      # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: ADO
        institute: Adobe Research
        location: Adobe Research, United States

    - lastname: Parag Shah
      firstname: Ankit
      email: aps1@andrew.cmu.edu                      # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: CMU
        institute: Carnegie Mellon University
        department: School of Computer Science
        location: Pittsburgh, United States

    - lastname: Eghbal-Zadeh
      firstname: Hamid
      email: hamid.eghbal-zadeh@jku.at                # Contact email address

      # Affiliation information for the author
      affiliation:
        abbreviation: JKP
        institute: Johannes Kepler University
        department: Department of Computational Perception
        location: Linz, Austria

# System information
system:
  # System description, meta data provided here will be used to do
  # meta analysis of the submitted system. Use general level tags, if possible use the tags provided in comments.
  # If information field is not applicable to the system, use "!!null".
  description:

    # Audio input
    input_channels: mono                  # e.g. one or multiple [mono, binaural, left, right, mixed, ...]
    input_sampling_rate: 44.1kHz          #

    # Acoustic representation
    acoustic_features: log-mel energies   # e.g one or multiple [MFCC, log-mel energies, spectrogram, CQT, ...]

    # Data augmentation methods
    data_augmentation: !!null             # [time stretching, block mixing, pitch shifting, ...]

    # Machine learning
    # In case using ensemble methods, please specify all methods used (comma separated list).
    machine_learning_method: CRNN         # e.g one or multiple [GMM, HMM, SVM, kNN, MLP, CNN, RNN, CRNN, NMF, random forest, ensemble, ...]

    # Ensemble method subsystem count
    # In case ensemble method is not used, mark !!null.
    ensemble_method_subsystem_count: !!null # [2, 3, 4, 5, ... ]

    # Decision making methods
    decision_making: !!null                 # [majority vote, ...]

    # Semi-supervised method used to exploit both labelled and unlabelled data
    machine_learning_semi_supervised: mean-teacher student         # e.g one or multiple [pseudo-labelling, mean-teacher student...]

    # Segmentation method
    segmentation_method: !!null                             # E.g. [RBM, attention layers...]

    # Post-processing, followed by the time span (in ms) in case of smoothing
    post-processing: median filtering (93ms)                # [median filtering, time aggregation...]

  # System complexity, meta data provided here will be used to evaluate
  # submitted systems from the computational load perspective.
  complexity:

    # Total amount of parameters used in the acoustic model. For neural networks, this
    # information is usually given before training process in the network summary.
    # For other than neural networks, if parameter count information is not directly available,
    # try estimating the count as accurately as possible.
    # In case of ensemble approaches, add up parameters for all subsystems.
    total_parameters: 126090
    # Approximate training time followed by the hardware used
    trainining_time: 3h (1 GTX 1080 Ti)

  # The training subsets used to train the model. Followed the amount of data (number of clips) used per subset.
  subsets:              # [weak (xx), unlabel_in_domain (xx), synthetic (xx)]

  # URL to the source code of the system [optional, highly recommended]
  source_code: https://github.com/DCASE-REPO/dcase2018_baseline/tree/master/task4/

# System results
results:
  # Full results are not mandatory, but for through analysis of the challenge submissions recommended.
  # If you cannot provide all results, also incomplete results can be reported.

  development_dataset:
    # System result for development dataset with provided the cross-validation setup.
    overall:
      F-score: 23.7

    # Class-wise accuracies
    class_wise:
      Alarm_bell_ringing:
        F-score: 20.0
      Blender:
        F-score: 25.1
      Cat:
        F-score: 7.2
      Dishes:
        F-score: 3.2
      Dog:
        F-score: 14.1
      Electric_shaver_toothbrush:
        F-score: 42.7
      Frying:
        F-score: 54.1
      Running_water:
        F-score: 11.6
      Speech:
        F-score: 21.5
      Vacuum_cleaner:
        F-score: 60.8
                

Example meta information file for Task 5 baseline system task5/Cartwright_NYU_task5_1/Cartwright_NYU_task5_1.meta.yaml:

# Submission information
submission:
  # Submission label
  # Label is used to index submissions, to avoid overlapping codes among submissions
  # use following way to form your label:
  # [Last name of corresponding author]_[Abbreviation of institution of the corresponding author]_task[task number]_[index number of your submission (1-4)]
  label: Cartwright_NYU_task5_1

  # Submission name
  # This name will be used in the results tables when space permits
  name: DCASE2019 baseline system

  # Submission name abbreviated
  # This abbreviated name will be used in the results table when space is tight, maximum 10 characters
  abbreviation: Baseline

  # Submission authors in order, mark one of the authors as corresponding author.
  authors:
    # First author
    - firstname: Mark
      lastname: Cartwright
      email: mark.cartwright@nyu.edu                 # Contact email address
      corresponding: true                         # Mark true for one of the authors
      # Affiliation information for the author
      affiliation:
        institution: New York University
        department: Music and Audio Research Laboratory, Department of Computer Science and Engineering, Center for Urban Science and Progress
        location: New York, New York, USA

    # Second author
    - firstname: Jason
      lastname: Cramer
      # Affiliation information for the author
      affiliation:
        institution: New York University
        department: Music and Audio Research Laboratory, Department of Electrical and Computer Engineering
        location: New York, New York, USA

    # Third author
    - firstname: Ana Elisa
      lastname: Mendez Mendez
      # Affiliation information for the author
      affiliation:
        institution: New York University
        department: Music and Audio Research Laboratory, Department of Music and Performing Arts Professions
        location: New York, New York, USA

    # Fourth author
    - firstname: Ho-Hsiang
      lastname: Wu
      # Affiliation information for the author
      affiliation:
        institution: New York University
        department: Music and Audio Research Laboratory, Department of Music and Performing Arts Professions
        location: New York, New York, USA

    # Fifth author
    - firstname: Vincent
      lastname: Lostanlen
      # Affiliation information for the author
      affiliation:
        institution: Cornell University
        department: Cornell Lab of Ornithology
        location: Ithaca, New York, USA

    # Sixth author
    - firstname: Juan P.
      lastname: Bello
      # Affiliation information for the author
      affiliation:
        institution: New York University
        department: Music and Audio Research Laboratory, Department of Computer Science and Engineering, Center for Urban Science and Progress
        location: New York, New York, USA

    # Seventh author
    - firstname: Justin
      lastname: Salamon
      # Affiliation information for the author
      affiliation:
        institution: Adobe Research
        department: Machine Perception Team
        location: San Francisco, CA, USA


# System information
system:
  # System description, meta data provided here will be used to do
  # meta analysis of the submitted system. Use general level tags, if possible use the tags provided in comments.
  # If information field is not applicable to the system, use "!!null".
  description:

    # Audio input
    input_channels: mono                  # e.g. one or multiple [mono, binaural, left, right, mixed, ...]
    input_sampling_rate: 44.1kHz            #

    # Acoustic representation
    acoustic_features: vggish   # e.g one or multiple [MFCC, log-mel energies, spectrogram, CQT, deep embedding (e.g. vggish), ...]

    # Data augmentation methods
    data_augmentation: !!null             # [time stretching, block mixing, pitch shifting, ...]

    # Machine learning method
    # In case using ensemble methods, please specify all methods used (comma separated list).
    # You do not need to repeat model types used multiple times in the ensemble.
    machine_learning_method: logistic regression          # e.g one or multiple [GMM, HMM, kNN, MLP, CNN, RNN, CRNN, NMF, random forest, ensemble, ...]

    # Ensemble method subsystem count
    # In case ensemble method is not used, mark !!null.
    ensemble_method_subsystem_count: !!null # [2, 3, 4, 5, ... ]

    # Specify if any of the additional metadata was used for training
    used_annotator_id: false
    used_proximity: false
    used_sensor_id: false

    # Method for aggregating predictions over time, if relevant
    aggregation_method: !!null               # [mean, ...]

    # External data approach
    external_data: !!null                 # [pre-trained model, audio data, ...]

    # Comma separated list of external data sources used
    external_data_sources: !!null                 # [pre-trained model, audio data, ...]

    # Annotation level targeted by model
    # That is, if the model only predicts fine level annotations
    # should be specified as "fine". A model that is specifically
    # trained to predict both fine and coarse annotations should
    # be specified as "both".
    target_level: fine                      # [fine, coarse, both]

    # Method for determining targets for training from annotations
    target_method: minority vote          # [minority vote, majority vote, ...]

    # Re-labeling of the train set
    re_labeling: !!null                  # [automatic, manual, ...]

    # NOTE: These should only be provided if providing detections, rather than
    # probabilities of class presence.
    #
    # Type of method used to determine detection thresholds
    detection_threshold_method: !!null     # [automatic, manual, fixed, !!null]
    # Specify if the method for determining threshold was done over all classes
    # or per class
    detection_threshold_level: !!null     # [global, classwise, !!null]

  # System complexity, meta data provided here will be used to evaluate
  # submitted systems from the computational load perspective.
  complexity:

    # Total amount of learned parameters used in the acoustic model.
    # For neural networks, this information is usually given before training process
    # in the network summary. For other than neural networks, if parameter count
    # information is not directly available,try estimating the count as accurately
    # as possible. In case of ensemble approaches, add up parameters for all subsystems.
    total_parameters: 2967

  # URL to the source code of the system
  source_code: https://github.com/sonyc-project/urban-sound-tagging-baseline