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

作　　者：Rulin ZHANG Ruixue LI Jiakai LIANG Keqiang YUE Wenjun LI Yilin LI 

机构地区：[1]Electronics and Information College,Hangzhou Dianzi University,Hangzhou 310018,China

出　　处：《Chinese Journal of Electronics》2024年第3期793-802,共10页电子学报（英文版）

基　　金：supported by the Zhejiang Key Research and Development Project (Grant No.2022C01048);the Zhejiang Province Public Welfare Project (Grant No.LGG22F010012)。

摘　　要：Snoring is a widespread occurrence 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%,accompanied by a remarkable 36.9%reduction in computer power compared to the regular LSTM model.

关 键 词：Snoring detect Mel frequency cepstral coefficients Spiking encoding Long short-term memory spiking neural networks Backpropagation based on alternative gradient 

分 类 号：TN912.3[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程] R318[自动化与计算机技术—控制理论与控制工程]