@inproceedings{1cd2c30b3d574a2a89e4cc9d4fcd341c,
title = "Snore Sound Recognition via an Explainable Capsule Network",
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.",
keywords = "Capsule Network, Computer Audition, Digital Health, Explainable Model, Snore Sound Classification",
author = "Cho, {Min Ki} and Zhonghao Zhao and Zhihua Wang and Kun Qian and Bin Hu and Yoshiharu Yamamoto and Schuller, {Bj{\"o}rn W.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE Global Conference on Consumer Electronics, GCCE 2023 ; Conference date: 10-10-2023 Through 13-10-2023",
year = "2023",
doi = "10.1109/GCCE59613.2023.10315619",
language = "English",
series = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1048--1049",
booktitle = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
address = "United States",
}