Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement  

作　　者：Xiaojun Zhu Heming Huang 

机构地区：[1]School of Computer Science,Qinghai Normal University,Xining,810008,China [2]The State Key Laboratory of Tibetan Intelligent Information Processing and Application,Xining,810008,China [3]School of Electronic and Information Engineering,Lanzhou City University,Lanzhou,730000,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第6期2155-2172,共18页工程与科学中的计算机建模（英文）

基　　金：supported by the National Science Foundation under Grant No.62066039.

摘　　要：Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals.However,the training of Generative Adversarial Networks has such problems as convergence difficulty,model collapse,etc.In this work,an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed,and some improvements have been made in order to get faster convergence speed and better generated speech quality.Specifically,in the generator coding part,each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales;a gated linear unit is introduced to alleviate the vanishing gradient problem with the increase of network depth;the gradient penalty of the discriminator is replaced with spectral normalization to accelerate the convergence rate of themodel;a hybrid penalty termcomposed of L1 regularization and a scale-invariant signal-to-distortion ratio is introduced into the loss function of the generator to improve the quality of generated speech.The experimental results on both TIMIT corpus and Tibetan corpus show that the proposed model improves the speech quality significantly and accelerates the convergence speed of the model.

关 键 词：Speech enhancement generative adversarial networks hybrid penalty gated linear units multi-scale convolution 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]