Independent Low-Rank Matrix Analysis Based on Parametric Majorization-Equalization Algorithm

TL;DR

This paper introduces a parametric majorization-equalization algorithm to improve the convergence and performance of independent low-rank matrix analysis for blind source separation.

Contribution

It proposes a novel parameter to balance the convergence speeds of spatial and source models in ILRMA, enhancing optimization efficiency.

Findings

Improved convergence speed in ILRMA.

Better separation quality in blind source separation.

Flexible control over model optimization balance.

Abstract

In this paper, we propose a new optimization method for independent low-rank matrix analysis (ILRMA) based on a parametric majorization-equalization algorithm. ILRMA is an efficient blind source separation technique that simultaneously estimates a spatial demixing matrix (spatial model) and the power spectrograms of each estimated source (source model). In ILRMA, since both models are alternately optimized by iterative update rules, the difference in the convergence speeds between these models often results in a poor local solution. To solve this problem, we introduce a new parameter that controls the convergence speed of the source model and find the best balance between the optimizations in the spatial and source models for ILRMA.