Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation

TL;DR

This paper introduces a computationally efficient blind source separation method combining WPE dereverberation and independent vector extraction, significantly reducing processing time while maintaining separation quality in noisy reverberant environments.

Contribution

It presents a novel integration of WPE and IVE, simplifying the optimization process for faster blind source separation in reverberant settings.

Findings

Achieves faster convergence than conventional methods

Maintains separation performance in reverberant environments

Reduces computational cost for large microphone arrays

Abstract

We address a blind source separation (BSS) problem in a noisy reverberant environment in which the number of microphones $M$ is greater than the number of sources of interest, and the other noise components can be approximated as stationary and Gaussian distributed. Conventional BSS algorithms for the optimization of a multi-input multi-output convolutional beamformer have suffered from a huge computational cost when $M$ is large. We here propose a computationally efficient method that integrates a weighted prediction error (WPE) dereverberation method and a fast BSS method called independent vector extraction (IVE), which has been developed for less reverberant environments. We show that, given the power spectrum for each source, the optimization problem of the new method can be reduced to that of IVE by exploiting the stationary condition, which makes the optimization easy to handle and computationally efficient. An experiment of speech signal separation shows that, compared to a conventional method that integrates WPE and independent vector analysis, our proposed method achieves much faster convergence while maintaining its separation performance.