# Acceleration of rank-constrained spatial covariance matrix estimation for blind speech extraction

## TL;DR

This paper introduces an accelerated update rule for rank-constrained spatial covariance matrix estimation, significantly reducing computational complexity and enabling faster blind speech extraction in noisy environments.

## Contribution

The paper presents a novel method that eliminates matrix inversion in covariance estimation, achieving 87 times faster computation than traditional approaches.

## Key findings

- Achieves 87x faster computation
- Effective extraction of target speech in diffuse noise
- Reduces computational complexity in covariance matrix estimation

## Abstract

In this paper, we propose new accelerated update rules for rank-constrained spatial covariance model estimation, which efficiently extracts a directional target source in diffuse background noise.The naive updat e rule requires heavy computation such as matrix inversion or matrix multiplication. We resolve this problem by expanding matrix inversion to reduce computational complexity; in the parameter update step, we need neither matrix inversion nor multiplication. In an experiment, we show that the proposed accelerated update rule achieves 87 times faster calculation than the naive one.