Speech Signal Separation Based on Generative Adversarial Networks
摘要
The single-channel speech separation based on deep learning needs to calculate the time-frequency masking,which,however,cannot be learnt in the existing methods.Moreover,the time-frequency masking is not encapsulated in in-depth learning for optimization,so it relies on Wiener filtering for subsequent processing.Therefore,this paper proposes a speech signal separation method based on Generative Adversarial Networks(GAN).In the speech generation stage,the recursive derivation algorithm and sparse encoder are introduced to improve the time-frequency generation results.Then,the generated speach is eatered into the discriminator for classification,so as to reduce the disturbance between signal sources.The experimental results show that compared with other speech signal separation methods,such as the codec-based method and the recurrent neural network-based method,the SDR and SIR separation indexes of the proposed method increase by 6.2 dB and 5.0 dB respectively.