摘要
针对基于非负矩阵分解的心肺音分离方法假设心肺音在时频域上是线性混叠的,且没有利用心肺音成分的时序相关性等问题,提出基于全连接LSTM的心肺音分离方法。将长短时记忆网络应用于心肺音分离,以处理心肺音成分的非线性混叠,并捕捉心肺音成分的时序相关性,加强分离效果。为减少网络参数,提高训练速度,LSTM网络采用全连接网络结构。实验结果表明:本文设计的LSTM网络取得了优于监督非负矩阵分解方法的心肺音分离效果。
In order to address the interference of cardiac sounds and respiratory sounds,researchers proposed a method for separating the cardiorespiratory sound based on non-negative matrix factorization (NMF).However,this method assumes that the cardiac sounds and respiratory sounds are mixed in a linear method in the time-frequency domain,and it didn’t utilize the temporal correlation of cardiorespiratory sounds.Therefore,in this paper we applied the long short-time memory (LSTM) network to the separation of cardiorespiratory sounds to deal with the nonlinear mixing of cardiorespiratory sounds and capture the temporal correlation to enhance the separation performances.In order to reduce the network parameters and improve speed of training,LSTM were constructed in a fully connected structure.The experimental results show that the LSTM network achieves better performances of cardiorespiratory sound separation than the supervised NMF method.
作者
雷志彬
陈骏霖
Lei Zhibin;Chen Junlin(Guangdong University of Technology)
出处
《自动化与信息工程》
2018年第6期25-30,共6页
Automation & Information Engineering
关键词
心肺音分离
非负矩阵分解
长短时记忆网络
Cardiorespiratory Sound Separation
Non-Negative Matrix Factorization
Long Short-Time Memory Network
