Accelerating Auxiliary Function-based Independent Vector Analysis

TL;DR

This paper proposes techniques to accelerate the convergence of AuxIVA update rules in Independent Vector Analysis, enhancing real-time blind source separation of audio signals without increasing computational cost.

Contribution

It introduces novel methods to speed up AuxIVA convergence, improving real-time BSS performance without additional computational burden.

Findings

Accelerated AuxIVA convergence demonstrated in real-world acoustic scenarios.

Methods achieve faster convergence without extra computational cost.

Enhanced suitability for real-time audio source separation applications.

Abstract

Independent Vector Analysis (IVA) is an effective approach for Blind Source Separation (BSS) of convolutive mixtures of audio signals. As a practical realization of an IVA-based BSS algorithm, the so-called AuxIVA update rules based on the Majorize-Minimize (MM) principle have been proposed which allow for fast and computationally efficient optimization of the IVA cost function. For many real-time applications, however, update rules for IVA exhibiting even faster convergence are highly desirable. To this end, we investigate techniques which accelerate the convergence of the AuxIVA update rules without extra computational cost. The efficacy of the proposed methods is verified in experiments representing real-world acoustic scenarios.