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

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Computer Modeling in Engineering & sciences 2023年第6期135卷 2155-2172页

作者： Xiaojun Zhu Heming Huang School of Computer science Qinghai Normal UniversityXining810008China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008China School of Electronic and Information Engineering Lanzhou City UniversityLanzhou730000China

Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy ***,the training of Generative Adversarial Networks has such problems as convergence difficulty,model collapse,*** 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 ***,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 *** 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

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