FastMVAE2: On improving and accelerating the fast variational autoencoder-based source separation algorithm for determined mixtures

TL;DR

This paper introduces a new source model and training scheme that enhance the accuracy and speed of the FastMVAE algorithm for multichannel source separation, especially in unseen data scenarios.

Contribution

The paper proposes the ChimeraACVAE model and a knowledge distillation training scheme to improve generalization and efficiency of FastMVAE.

Findings

Achieved better source separation performance with less computation time.

Successfully separated 18 sources with good accuracy.

Enhanced generalization capability of the source model.

Abstract

This paper proposes a new source model and training scheme to improve the accuracy and speed of the multichannel variational autoencoder (MVAE) method. The MVAE method is a recently proposed powerful multichannel source separation method. It consists of pretraining a source model represented by a conditional VAE (CVAE) and then estimating separation matrices along with other unknown parameters so that the log-likelihood is non-decreasing given an observed mixture signal. Although the MVAE method has been shown to provide high source separation performance, one drawback is the computational cost of the backpropagation steps in the separation-matrix estimation algorithm. To overcome this drawback, a method called "FastMVAE" was subsequently proposed, which uses an auxiliary classifier VAE (ACVAE) to train the source model. By using the classifier and encoder trained in this way, the optimal parameters of the source model can be inferred efficiently, albeit approximately, in each step of the algorithm. However, the generalization capability of the trained ACVAE source model was not satisfactory, which led to poor performance in situations with unseen data. To improve the generalization capability, this paper proposes a new model architecture (called the "ChimeraACVAE" model) and a training scheme based on knowledge distillation. The experimental results revealed that the proposed source model trained with the proposed loss function achieved better source separation performance with less computation time than FastMVAE. We also confirmed that our methods were able to separate 18 sources with a reasonably good accuracy.