Joint Separation and Dereverberation of Reverberant Mixtures with Multichannel Variational Autoencoder

Abstract

In this paper, we deal with a multichannel source separation problem under a highly reverberant condition. The multichan- nel variational autoencoder (MVAE) is a recently proposed source separation method that employs the decoder distribu- tion of a conditional VAE (CVAE) as the generative model for the complex spectrograms of the underlying source sig- nals. Although MVAE is notable in that it can significantly improve the source separation performance compared with conventional methods, its capability to separate highly rever- berant mixtures is still limited since MVAE uses an instan- taneous mixture model. To overcome this limitation, in this paper we propose extending MVAE to simultaneously solve source separation and dereverberation problems by formulat- ing the separation system as a frequency-domain convolutive mixture model. A convergence-guaranteed algorithm based on the coordinate descent method is derived for the optimiza- tion. Experimental results revealed that the proposed method outperformed the conventional methods in terms of all the source separation criteria in highly reverberant environments.