  This paper proposes an alternative algorithm for multichannel variational
autoencoder (MVAE), a recently proposed multichannel source separation
approach. While MVAE is notable in its impressive source separation
performance, the convergence-guaranteed optimization algorithm and that it
allows us to estimate source-class labels simultaneously with source
separation, there are still two major drawbacks, i.e., the high computational
complexity and unsatisfactory source classification accuracy. To overcome these
drawbacks, the proposed method employs an auxiliary classifier VAE, an
information-theoretic extension of the conditional VAE, for learning the
generative model of the source spectrograms. Furthermore, with the trained
auxiliary classifier, we introduce a novel algorithm for the optimization that
is able to not only reduce the computational time but also improve the source
classification performance. We call the proposed method "fast MVAE (fMVAE)".
Experimental evaluations revealed that fMVAE achieved comparative source
separation performance to MVAE and about 80% source classification accuracy
rate while it reduced about 93% computational time.
