LI Hui1, JING Hao2, YAN Kanghua2, ZOU Borong1, HOU Qinghua1, WU Huibin1,et al.Speech enhancement method based on dual-channel convolutional attention network[J].Journal of Henan Polytechnic University(Natural Science) ,2022,41(5):127-136.

doi:10.16186/j.cnki.1673-9787.2020060014

Received:2020/06/04

Revised:2021/12/06

Published:2022/09/25

Speech enhancement method based on dual-channel convolutional attention network

LI Hui1, JING Hao2, YAN Kanghua2, ZOU Borong1, HOU Qinghua1, WU Huibin1

1.School of Physics & Electronic Information Engineering，Henan Polytechnic University，Jiaozuo  454000，Henan，China<br/>2.School of Electrical Engineering and Automation，Henan Polytechnic University，Jiaozuo  454000，Henan，China

Abstract:The traditional single-channel network model is unable to fully extract the deep features of speech due to its limited representation ability，resulting in insignificant enhancement effect.In view of this，a dual-channel convolutional attention network speech enhancement method was proposed.Firstly，the convolutional neural network and long short-term memory network were used to construct a parallel dual-channel learning module.The dual-channel learning module could combine the advantages of the two different neural networks to fully explore the deep features of speech.Secondly，attention module was added in each channel to weight the output features of the channel according to the degree of attention.Finally，enhanced features were obtained by fusing the output of the two channels.Experimental results showed that，in low SNR and non-stationary noise environment，the enhanced effect of the model including dual-channel structure and attention module was obviously better than other contrast models，which effectively improved the quality and intelligibility of enhanced speech，and further confirmed the feasibility of the proposed model.

Key words:speech enhancement;convolutional neural network;long short-term memory network;dual-channel learning module;attention module

基于双通道卷积注意力网络的语音增强方法_李辉.pdf