Long Short-Term Memory Spiking Neural Networks for Classification of Snoring and Non-snoring Sound Events

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外文摘要：Snoring is a widespreadoccurrence that impacts human sleep quality.It is also one of the earliest symptoms of many sleep disorders.Snoring is accurately detected,making further screening and diagnosis of sleep problems easier.Snoring is frequently ignored because of its underrated and costly detection costs.As a result,this research offered an alternative method for snoring detection based on a long short-term memory based spiking neural network(LSTM-SNN)that is appropriate for large-scale home detection for snoring.We designed acquisition equipment to collect the sleep recordings of 54 subjects and constructed the sleep sound database in the home environment.And Mel frequency cepstral coefficients(MFCCs)were extracted from these sound signals and encoded into spike trains by a threshold encoding approach.They were classified automatically as non-snoring or snoring sounds by our LSTM-SNN model.We used the backpropagation algorithm based on an alternative gradient in the LSTM-SNN to complete the parameter update.The categorization percentage reached an impressive 93.4％,accom-panied by a remarkable 36.9％ reduction in computer power compared to the regular LSTM model.

外文关键词：

Snoring detectMel frequency cepstral coefficientsSpiking encodingLong short-term memory spiking neural networksBackpropagation based on alternative gradient

作者：

Rulin ZHANG、Ruixue LI、Jiakai LIANG、Keqiang YUE、Wenjun LI、Yilin LI

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作者单位：

Electronics and Information College,Hangzhou Dianzi University,Hangzhou 310018,China

基金：

Zhejiang Key Research and Development ProjectZhejiang Province Public Welfare Project

项目编号：

2022C01048LGG22F010012

出版年：

2024

DOI：

10.23919/cje.2022.00.210

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(3)