Snore Sound Recognition via an Explainable Capsule Network

Minki Cho, Zhonghao Zhao, Zhihua Wang, Kun Qian, Bin Hu, Yoshiharu Yamamoto, Björn W. Schuller

Abstract: Snoring can be caused by an upper airway reaction while sleeping. Classifying the excitation locations of snore sounds accurately provides assistance for treating snoring. In this research, we propose a convolutional neural network combined with a Capsule Network (CapsNet) to solve this problem. The models were trained and tested on the Munich-Passau Snore Sound Corpus (MPSSC), a relatively small and imbalanced dataset that contains four classes. As a result, the proposed method achieved an Unweighted Average Recall (UAR) of 58.5 %. Furthermore, we explained the working principle of the CapsNet through visualization, which could be helpful for understanding the generation of the results.