Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge is one of the most important international challenges concerning acoustic event detection and classification. The main procedure of acoustic activity classification consists of four parts: pre-processing, extracting acoustic features, designing acoustic models as classifiers, and postprocessing. In recent years, Convolutional Neural Networks (CNNs) have achieved great success in many fields such as character recognition, image classification, speaker recognition. And many works based on CNNs have been done in acoustic event classification and detection .

Papers:

Ryo Tanabe, Takashi Endo, Yuki Nikaido, Takeshi Ichige, Phong Nguyen, Yohei Kawaguchi and Koichi Hamada, “Multichannel acoustic scene classification by blind dereverberation, blind source separation, data augmentation, and model ensembling, ” DCASE 2018 Challenge, Technical Report, 2018.

Gert Dekkers, Lode Vuegen, Toon van Waterschoot, Bart Vanrumste, and Peter Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” Technical Report, KU Leuven, 2018. URL: https://arxiv.org/abs/1807.11246, arXiv:1807.11246.

Tadanobu Inoue, Phongtharin Vinayavekhin, Shiqiang Wang, David Wood, Nancy Greco and Ryuki Tachibana, “Domestic Activities Classification Based on CNN Using Shuffling and Mixing Data Augmentation,” DCASE 2018 Challenge, Technical Report, 2018.