Open AccessSpeech Dereverberation Based on Improved Wasserstein Generative Adversarial Networks
Author(s) -
Lufang Rao,
Junmei Yang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1621/1/012089
Subject(s) - computer science , reverberation , discriminator , speech recognition , generalization , generative grammar , stability (learning theory) , speech processing , generator (circuit theory) , artificial intelligence , machine learning , acoustics , mathematics , telecommunications , mathematical analysis , power (physics) , physics , quantum mechanics , detector
In reality, the sound we hear is not only disturbed by noise, but also the reverberant, whose effects are rarely taken into account. Recently, deep learning has shown great advantages in speech signal processing. But among the existing dereverberation approaches, very few methods apply deep learning at the waveform level. In addition, in the case of sever reverberation, the conventional speech dereverberation methods perform poorly, such as MCLP (multi-channel linear prediction). We proposed a new speech dereverberation method in this paper, which is based on improved WGAN (Wasserstein Generative Adversarial Networks), called WGAN-GP, whose generator uses strided-convolutional networks and the discriminator is structured on DNNs. Due to the addition of the gradient penalty item, WGAN-GP improves the stability of training and the generalization of the model. In the case of severe reverberation, according to the experimental results, the proposed system can perform better than MCLP. As the proposed method based on WAGN-GP can improve speech quality, speech signal processing systems may be able to apply it to pre-processing stage.