Block Coordinate Descent Algorithms for Auxiliary-Function-Based Independent Vector Extraction

TL;DR

This paper introduces improved block coordinate descent algorithms for independent vector extraction, effectively handling super-Gaussian sources in noisy mixtures with faster convergence and robustness, especially in blind and semiblind scenarios.

Contribution

The paper develops enhanced BCD algorithms for IVE that exploit Gaussian noise stationarity and incorporate semiblind transfer function information, improving convergence and robustness.

Findings

Faster convergence of proposed BCD algorithms in numerical experiments.

Effective extraction of speech signals from noisy mixtures.

Robust performance in blind and semiblind extraction scenarios.

Abstract

In this paper, we address the problem of extracting all super-Gaussian source signals from a linear mixture in which (i) the number of super-Gaussian sources $K$ is less than that of sensors $M$, and (ii) there are up to $M - K$ stationary Gaussian noises that do not need to be extracted. To solve this problem, independent vector extraction (IVE) using a majorization minimization and block coordinate descent (BCD) algorithms has been developed, attaining robust source extraction and low computational cost. We here improve the conventional BCDs for IVE by carefully exploiting the stationarity of the Gaussian noise components. We also newly develop a BCD for a semiblind IVE in which the transfer functions for several super-Gaussian sources are given a priori. Both algorithms consist of a closed-form formula and a generalized eigenvalue decomposition. In a numerical experiment of extracting speech signals from noisy mixtures, we show that when $K = 1$ in a blind case or at least $K - 1$ transfer functions are given in a semiblind case, the convergence of our proposed BCDs is significantly faster than those of the conventional ones.