Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model

TL;DR

This paper introduces a novel time-variant sub-Gaussian source model for independent low-rank matrix analysis, enhancing blind audio source separation by improving model flexibility and convergence guarantees.

Contribution

It proposes a new GGD-ILRMA with a time-variant sub-Gaussian model and a generalized iterative projection update scheme, extending ILRMA's applicability and stability.

Findings

Demonstrates versatility across various source signals

Provides convergence-guaranteed update rules

Enhances separation performance with sub-Gaussian modeling

Abstract

Independent low-rank matrix analysis (ILRMA) is a fast and stable method for blind audio source separation. Conventional ILRMAs assume time-variant (super-)Gaussian source models, which can only represent signals that follow a super-Gaussian distribution. In this paper, we focus on ILRMA based on a generalized Gaussian distribution (GGD-ILRMA) and propose a new type of GGD-ILRMA that adopts a time-variant sub-Gaussian distribution for the source model. By using a new update scheme called generalized iterative projection for homogeneous source models, we obtain a convergence-guaranteed update rule for demixing spatial parameters. In the experimental evaluation, we show the versatility of the proposed method, i.e., the proposed time-variant sub-Gaussian source model can be applied to various types of source signal.