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A Multi-feature Sets Fusion Strategy with Similar Samples Removal for Snore Sound Classification

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Man-Machine Speech Communication (NCMMSC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1765))

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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 ‘classic’ features sets. At training stage, we eliminate selected samples to improve discrimination between different classes. As to unweighted average recall, a field’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.

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Acknowledgements

This work was partially supported by the Ministry of Science and Technology of the People’s Republic of China (2021ZD0201900), the National Natural Science Foundation of China (62272044), the BIT Teli Young Fellow Program from the Beijing Institute of Technology, China, and the Grants-in-Aid for Scientific Research (No. 20H00569) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.

Author information

Authors and Affiliations

  1. Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education (Beijing Institute of Technology), Beijing, 100081, China

    Zhonghao Zhao, Yang Tan, Mengkai Sun, Kun Qian & Bin Hu

  2. School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China

    Zhonghao Zhao, Yang Tan, Mengkai Sun, Kun Qian & Bin Hu

  3. GLAM – Group on Language, Audio, Music, Imperial College London, London, SW7 2AZ, UK

    Yi Chang & Björn W. Schuller

  4. Chair for Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, 86159, Augsburg, Germany

    Björn W. Schuller

  5. Graduate School of Education, The University of Tokyo, Tokyo, 113-0033, Japan

    Yoshiharu Yamamoto

Authors
  1. Zhonghao Zhao
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  2. Yang Tan
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  3. Mengkai Sun
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  4. Yi Chang
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  7. Björn W. Schuller
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Corresponding authors

Correspondence to Kun Qian or Bin Hu .

Editor information

Editors and Affiliations

  1. University of Science and Technology of China, Anhui, China

    Ling Zhenhua

  2. Hefei University, Anhui, China

    Gao Jianqing

  3. Shanghai Jiaotong University, Shanghai, China

    Yu Kai

  4. Tsinghua University, Beijing, China

    Jia Jia

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Zhao, Z. et al. (2023). A Multi-feature Sets Fusion Strategy with Similar Samples Removal for Snore Sound Classification. In: Zhenhua, L., Jianqing, G., Kai, Y., Jia, J. (eds) Man-Machine Speech Communication. NCMMSC 2022. Communications in Computer and Information Science, vol 1765. Springer, Singapore. https://doi.org/10.1007/978-981-99-2401-1_3

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  • DOI: https://doi.org/10.1007/978-981-99-2401-1_3

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  • Print ISBN: 978-981-99-2400-4

  • Online ISBN: 978-981-99-2401-1

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