Nico Gößling

Nico Gößling, Wiebke Middelberg, Simon Doclo

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.

N. Gößling, D. Marquardt, I. Merks et al.

Binaural beamforming algorithms for head-mounted assistive listening devices are crucial to improve speech quality and speech intelligibility in noisy environments, while maintaining the spatial impression of the acoustic scene. While the well-known BMVDR beamformer is able to preserve the binaural cues of one desired source, the BLCMV beamformer uses additional constraints to also preserve the binaural cues of interfering sources. In this paper, we provide theoretical and practical insights on how to optimally set the interference scaling parameters in the BLCMV beamformer for an arbitrary number of interfering sources. In addition, since in practice only a limited temporal observation interval is available to estimate all required beamformer quantities, we provide an experimental evaluation in a complex acoustic scenario using measured impulse responses from hearing aids in a cafeteria for different observation intervals. The results show that even rather short observation intervals are sufficient to achieve a decent noise reduction performance and that a proposed threshold on the optimal interference scaling parameters leads to smaller binaural cue errors in practice.

N. Gößling, S. Doclo

Many multi-microphone speech enhancement algorithms require the relative transfer function (RTF) vector of the desired speech source, relating the acoustic transfer functions of all array microphones to a reference microphone. In this paper, we propose a computationally efficient method to estimate the RTF vector in a diffuse noise field, which requires an additional microphone that is spatially separated from the microphone array, such that the spatial coherence between the noise components in the microphone array signals and the additional microphone signal is low. Assuming this spatial coherence to be zero, we show that an unbiased estimate of the RTF vector can be obtained. Based on real-world recordings experimental results show that the proposed RTF estimator outperforms state-of-the-art estimators using only the microphone array signals in terms of estimation accuracy and noise reduction performance.