  The binaural minimum-variance distortionless-response (BMVDR) beamformer is a
well-known noise reduction algorithm that can be steered using the relative
transfer function (RTF) vector of the desired speech source. Exploiting the
availability of an external microphone that is spatially separated from the
head-mounted microphones, an efficient method has been recently proposed to
estimate the RTF vector in a diffuse noise field. When multiple external
microphones are available, different RTF vector estimates can be obtained by
using this method for each external microphone. In this paper, we propose
several procedures to combine these RTF vector estimates, either by selecting
the estimate corresponding to the highest input SNR, by averaging the estimates
or by combining the estimates in order to maximize the output SNR of the BMVDR
beamformer. Experimental results for a moving speaker and diffuse noise in a
reverberant environment show that the output SNR-maximizing combination yields
the largest binaural SNR improvement and also outperforms the state-of-the art
covariance whitening method.
