@inproceedings{87a0eecc4f534306bd72fdfd1bd0319a,
title = "A Multi-feature Sets Fusion Strategy with Similar Samples Removal for Snore Sound Classification",
abstract = "Obstructive sleep apnoe (OSA) is a common clinical sleep-related breathing disorder. Classifying the excitation location of snore sound can help doctors provide more accurate diagnosis and complete treatment plans. In this study, we propose a strategy to classify snore sound leveraging {\textquoteleft}classic{\textquoteright} features sets. At training stage, we eliminate selected samples to improve discrimination between different classes. As to unweighted average recall, a field{\textquoteright}s major measure for imbalanced data, our method achieves 65.6 %, which significantly (p < 0.05, one-tailed z-test) outperforms the baseline of the INTERSPEECH 2017 ComParE Snoring Sub-challenge. Moreover, the proposed method can also improve the performance of other models based on the original classification results.",
keywords = "Computer Audition, Digital Health, Feature Fusion, Obstructive Sleep Apnea, Snore Sound Classification",
author = "Zhonghao Zhao and Yang Tan and Mengkai Sun and Yi Chang and Kun Qian and Bin Hu and Schuller, {Bj{\"o}rn W.} and Yoshiharu Yamamoto",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 17th National Conference on Man-Machine Speech Communication, NCMMSC 2022 ; Conference date: 15-12-2022 Through 18-12-2022",
year = "2023",
doi = "10.1007/978-981-99-2401-1_3",
language = "English",
isbn = "9789819924004",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "30--43",
editor = "Ling Zhenhua and Gao Jianqing and Yu Kai and Jia Jia",
booktitle = "Man-Machine Speech Communication - 17th National Conference, NCMMSC 2022, Proceedings",
address = "Germany",
}