# Efficient Full-Rank Spatial Covariance Estimation Using Independent Low-Rank Matrix Analysis for Blind Source Separation

## TL;DR

This paper introduces an efficient algorithm that enhances blind source separation by estimating full-rank spatial covariance, improving separation of directional sources and diffuse noise with reduced computational cost.

## Contribution

The paper presents a novel method that extends ILRMA to estimate full-rank spatial covariance, addressing limitations with diffuse noise separation.

## Key findings

- Improved separation performance in BSS tasks.
- Reduced computational cost compared to existing methods.
- Effective estimation of diffuse noise spatial basis.

## Abstract

In this paper, we propose a new algorithm that efficiently separates a directional source and diffuse background noise based on independent low-rank matrix analysis (ILRMA). ILRMA is one of the state-of-the-art techniques of blind source separation (BSS) and is based on a rank-1 spatial model. Although such a model does not hold for diffuse noise, ILRMA can accurately estimate the spatial parameters of the directional source. Motivated by this fact, we utilize these estimates to restore the lost spatial basis of diffuse noise, which can be considered as an efficient full-rank spatial covariance estimation. BSS experiments show the efficacy of the proposed method in terms of the computational cost and separation performance.