@techreport{Diment2013,
    Author = "Diment, Aleksandr and Heittola, Toni and Virtanen, Tuomas",
    title = "Sound Event Detection for Office Live and Office Synthetic {AASP} Challenge",
    institution = "DCASE2013 Challenge",
    year = "2013",
    month = "June",
    abstract = "We present a sound event detection system based on hidden Markov models. The system is evaluated with development material provided in the AASP Challenge on Detection and Classification of Acoustic Scenes and Events. Two approaches using the same basic detection scheme are presented. First one, developed for acoustic scenes with non-overlapping sound events is evaluated with Office Live development dataset. Second one, developed for acoustic scenes with some degree of overlapping sound events is evaluated with Office Synthetic development dataset."
}

@techreport{Gemmeke2013,
    Author = "Gemmeke, Jort F and Vuegen, Lode and Vanrumste, Bart and Van hamme, Hugo",
    title = "An Exemplar-Based {NMF} Approach for Audio Event Detection",
    institution = "DCASE2013 Challenge",
    year = "2013",
    month = "June",
    abstract = "We present a novel, exemplar-based method for audio event detection based on non-negative matrix factorisation (NMF). Building on recent work in noise robust automatic speech recognition, we model events as a linear combination of dictionary atoms, and mixtures as a linear combination of overlapping events. The exemplar based dictionary is created by extracting all available training data, artificially augmented by linear time warping at multiple rates. The method is evaluated on the Office Live and Office Synthetic development datasets released by the AASP Challenge on Detection and Classification of Acoustic Scenes and Events."
}

@techreport{Vuegen2013,
    Author = "Vuegen, Lode and Van Den Broeck, Bert and Karsmakers, Peter and Gemmeke, Jort F and Vanrumste, Bart and Van hamme, Hugo",
    title = "An {MFCC}-{GMM} Approach for Event Detection and Classification",
    institution = "DCASE2013 Challenge",
    year = "2013",
    month = "June",
    abstract = "This abstract explores Gaussian Mixture Models (GMM) estimated from Mel Frequency Cepstral Coefficients (MFCCs) for acoustic event detection and classification. To limit the impact of silence, a shared background model is used. An average Fscore of 48\% for the office life subtask is obtained. However, the analysis reveals that the proposed method has difficulties to cope with the large intra-class variations (e.g. time durations, dynamic range, characteristic sounds) in the provided dataset."
}