Xiaojun Zhu1,2,3, Heming Huang1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2155-2172, 2023, DOI:10.32604/cmes.2023.021453
- 23 November 2022
Abstract 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 More >
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