Robust Online Overdetermined Independent Vector Analysis Based on Bilinear Decomposition

TL;DR

This paper introduces a bilinear decomposition approach for online overdetermined independent vector analysis, significantly reducing parameters and enhancing robustness in large microphone array source separation.

Contribution

It proposes a novel bilinear decomposition of separation filters and an iterative update algorithm, improving online estimation accuracy and robustness for large arrays.

Findings

Fewer parameters lead to better performance.

The method outperforms existing approaches in robustness.

Achieves improved separation accuracy with large microphone arrays.

Abstract

Online blind source separation is essential for both speech communication and human-machine interaction. Among existing approaches, overdetermined independent vector analysis (OverIVA) delivers strong performance by exploiting the statistical independence of source signals and the orthogonality between source and noise subspaces. However, when applied to large microphone arrays, the number of parameters grows rapidly, which can degrade online estimation accuracy. To overcome this challenge, we propose decomposing each long separation filter into a bilinear form of two shorter filters, thereby reducing the number of parameters. Because the two filters are closely coupled, we design an alternating iterative projection algorithm to update them in turn. Simulation results show that, with far fewer parameters, the proposed method achieves improved performance and robustness.