RTF-steered Binaural MVDR Beamforming Incorporating an External Microphone for Dynamic Acoustic Scenarios

Abstract

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) vectors of the desired source. In this paper, we consider the recently proposed spatial coherence (SC) method to estimate the RTF vectors, requiring an additional external microphone that is spatially separated from the head-mounted microphones. Although the SC method provides a biased estimate of the RTF between the head-mounted microphones and the external microphone, we show that this bias is real-valued and only depends on the SNR in the external microphone. We propose to use the SC method to estimate the extended RTF vectors that also incorporate the external microphone, enabling to filter the external microphone signal in conjunction with the head-mounted microphones. Evaluation results using recorded signals of a moving speaker in diffuse noise show that the SC method yields a slightly better performance than the widely used covariance whitening method at a much lower computational complexity.