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 multichannel variational autoencoder (MVAE) is a recently proposed source separation method that employs the decoder distribution of a conditional VAE (CVAE) as the generative model for the complex spectrograms of the underlying source signals. Although MVAE is notable in that it can significantly improve the source separation performance compared with conventional methods, its capability to separate highly reverberant mixtures is still limited since MVAE uses an instantaneous mixture model. To overcome this limitation, in this paper we propose extending MVAE to simultaneously solve source separation and dereverberation problems by formulating 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.