RTF-Based Binaural MVDR Beamformer Exploiting an External Microphone in a Diffuse Noise Field

TL;DR

This paper introduces a new, efficient method for estimating the RTF vector in binaural noise reduction, utilizing an external microphone in diffuse noise fields to improve noise suppression and spatial cue preservation.

Contribution

It proposes a novel RTF estimation technique that leverages an external microphone, enhancing binaural MVDR beamforming performance in diffuse noise environments.

Findings

Outperforms existing RTF estimators in noise reduction

Better preservation of binaural cues demonstrated in real-world tests

Effective across various reverberation times

Abstract

Besides suppressing all undesired sound sources, an important objective of a binaural noise reduction algorithm for hearing devices is the preservation of the binaural cues, aiming at preserving the spatial perception of the acoustic scene. A well-known binaural noise reduction algorithm is the binaural minimum variance distortionless response beamformer, which can be steered using the relative transfer function (RTF) vector of the desired source, relating the acoustic transfer functions between the desired source and all microphones to a reference microphone. In this paper, we propose a computationally efficient method to estimate the RTF vector in a diffuse noise field, requiring an additional microphone that is spatially separated from the head-mounted microphones. Assuming that the spatial coherence between the noise components in the head-mounted microphone signals and the additional microphone signal is zero, we show that an unbiased estimate of the RTF vector can be obtained. Based on real-world recordings, experimental results for several reverberation times show that the proposed RTF estimator outperforms the widely used RTF estimator based on covariance whitening and a simple biased RTF estimator in terms of noise reduction and binaural cue preservation performance.