Microphone array post-filter for separation of simultaneous non-stationary sources

TL;DR

This paper introduces a microphone array post-filter that improves separation of simultaneous non-stationary sources, especially in mobile robotics, by modeling noise as stationary and transient components, outperforming existing methods.

Contribution

The proposed post-filter uniquely handles multiple simultaneous sources and non-stationary noise, enhancing separation quality beyond current single-source stationary noise models.

Findings

Better interference reduction than existing methods

Minimal signal distortion at low SNR

Effective in mobile robotics environments

Abstract

Microphone array post-filters have demonstrated their ability to greatly reduce noise at the output of a beamformer. However, current techniques only consider a single source of interest, most of the time assuming stationary background noise. We propose a microphone array post-filter that enhances the signals produced by the separation of simultaneous sources using common source separation algorithms. Our method is based on a loudness-domain optimal spectral estimator and on the assumption that the noise can be described as the sum of a stationary component and of a transient component that is due to leakage between the channels of the initial source separation algorithm. The system is evaluated in the context of mobile robotics and is shown to produce better results than current post-filtering techniques, greatly reducing interference while causing little distortion to the signal of interest, even at very low SNR.