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Classification of Snoring Sound-Related Signals Based on MLP

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Genetic and Evolutionary Computing (ICGEC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 834))

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Abstract

An efficient method to classify snore, breath sound and other noises based on the multilayer perceptron (MLP) was proposed in this paper. The spectral-related feature sets of the sound were extracted and used as the input feature of MLP. The minbatch training was designed to get the effective MLP model in training process. The dropout method was applied to optimize the structure of MLP. The correct rates for distinguishing snoring, breathing sounds, and other noises are 98.88%, 97.36%, and 95.15%, respectively.

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Acknowledgements

The authors thank the doctors and professors at the Department of Otolaryngology of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital for the recording data collection and segmentation. This study was funded by Science and Technology Commission of Shanghai Municipality (No. 13441901600) and National Natural Science Foundation of China (No. 61525203).

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Authors and Affiliations

  1. Shanghai University, Shanghai, 200444, China

    Limin Hou, Huancheng Liu, Xiaoyu Shi & Xinpeng Zhang

Authors
  1. Limin Hou
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  2. Huancheng Liu
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  3. Xiaoyu Shi
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  4. Xinpeng Zhang
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Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and it slater amendments or comparable ethical standards. It has been accepted for approval to the ethics committee of Shanghai sixth people’s hospital.

Corresponding author

Correspondence to Limin Hou .

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Editors and Affiliations

  1. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences, Bergen, China

    Jerry Chun-Wei Lin

  3. Changzhou College of Information Technology, Changzhou, Jiangsu, China

    Bixia Sui

  4. College of Smart Living and Management, Tajen University, Pingtung, Taiwan

    Shih-Pang Tseng

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Hou, L., Liu, H., Shi, X., Zhang, X. (2019). Classification of Snoring Sound-Related Signals Based on MLP. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_65

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