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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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.
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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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DOI: https://doi.org/10.1007/978-981-13-5841-8_65
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