NoisyILRMA: Diffuse-Noise-Aware Independent Low-Rank Matrix Analysis for Fast Blind Source Extraction

TL;DR

NoisyILRMA is a fast blind source extraction method that effectively handles diffuse noise by modifying ILRMA, incorporating a source model switching mechanism, and outperforming existing algorithms in speed while maintaining performance.

Contribution

The paper introduces NoisyILRMA, a novel diffuse-noise-aware BSE method that accelerates extraction and enhances performance through a source model switching mechanism.

Findings

NoisyILRMA runs faster than FastMNMF.

Switching mechanism improves BSE performance.

Maintains extraction quality comparable to existing methods.

Abstract

In this paper, we address the multichannel blind source extraction (BSE) of a single source in diffuse noise environments. To solve this problem even faster than by fast multichannel nonnegative matrix factorization (FastMNMF) and its variant, we propose a BSE method called NoisyILRMA, which is a modification of independent low-rank matrix analysis (ILRMA) to account for diffuse noise. NoisyILRMA can achieve considerably fast BSE by incorporating an algorithm developed for independent vector extraction. In addition, to improve the BSE performance of NoisyILRMA, we propose a mechanism to switch the source model with ILRMA-like nonnegative matrix factorization to a more expressive source model during optimization. In the experiment, we show that NoisyILRMA runs faster than a FastMNMF algorithm while maintaining the BSE performance. We also confirm that the switching mechanism improves the BSE performance of NoisyILRMA.