Comparative Assessment of Data Augmentation for Semi-Supervised Polyphonic Sound Event Detection

@article{DelphinPoulat2020ComparativeAO,
  title={Comparative Assessment of Data Augmentation for Semi-Supervised Polyphonic Sound Event Detection},
  author={Lionel Delphin-Poulat and Rozenn Nicol and Cyril Plapous and Katell Peron},
  journal={2020 27th Conference of Open Innovations Association (FRUCT)},
  year={2020},
  pages={46-53}
}
In the context of audio ambient intelligence systems in Smart Buildings, polyphonic Sound Event Detection aims at detecting, localizing and classifying any sound event recorded in a room. Today, most of models are based on Deep Learning, requiring large databases to be trained. We propose a CRNN system exploiting unlabeled data with semi-supervised learning based on the “Mean teacher” method, in combination with data augmentation to overcome the limited size of the training dataset and to… 
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