重庆邮电大学学报（自然科学版）2019，Vol.31Issue(1)：136-142,7.DOI:10.3979/j.issn.1673-825X.2019.01.018
基于WGAN的语音增强算法研究
Algorithm research of speech enhancement based on WGAN
摘要
Abstract
Noisy speech can be seen as a combination of an independent noise signal and a speech signal in some way. Traditional speech enhancement techniques need to make assumptions of the independence and feature distribution of noisy and clean speech signals. Unreasonable assumptions may cause problems such as residue noise and speech distortion, resulting in poor speech enhancement. In addition, the randomness and mutability of noise itself also affect the robustness of traditional speech enhancement methods. To solve these problems, this paper uses the generative adversarial network to enhance the speech, and gives a speech enhancement method based on the WGAN to accelerate the training speed and stabilize the training process. The method does not need to manually extract acoustic features, and it improves generalization capability of the speech enhancement system. There is a good enhancement effect in both the matched noise set and the unmatched noise set. The experimental results show that the PESQ is increased by an average of 23. 97％ based on this end to end speech enhancement training model.关键词
语音增强/生成对抗网络/卷积神经网络/深度学习Key words
speech enhancement/generative adversarial nets/convolution neural network/deep learning分类
信息技术与安全科学引用本文复制引用
王怡斐,韩俊刚,樊良辉..基于WGAN的语音增强算法研究[J].重庆邮电大学学报（自然科学版）,2019,31(1):136-142,7.基金项目
国家自然科学基金重点资助项目 (61136002) （61136002）
