Binaural Speaker Localization Integrated Into an Adaptive Beamformer for Hearing Aids
Mehdi Zohourian, Gerald Enzner, Rainer Martin · IEEE/ACM Transactions on Audio Speech and Language Processing · 2017
In this paper, we present and compare novel algorithms to localize simultaneous speakers using four microphones distributed on a pair of binaural hearing aids. The framework consists of two groups of localization algorithms, namely, beamforming-based and statistical model based localization algorithms. We first generalize our previously proposed methods based on beamforming techniques to the binaural configuration with 2 × 2 microphones. Next, we contribute two statistical model based methods for binaural localization using the maximum likelihood approach that also takes head-related transfer functions and unknown noise conditions into account. The methods enable the localization of multiple source positions for all azimuth angles and do not require prior training of binaural cues. The proposed localization algorithms are integrated into a generalized side-lobe canceller (GSC) to extract the desired speaker in the presence of competing speakers and background noise and when the head of the listener turns. The GSC components are adapted with the frequency-wise target presence probability and the frame-wise broadband direction-of-arrival (DOA) estimates that track the turns of the listener's head. We evaluate the performance of the localization algorithms individually and also in the context of the adaptive binaural beamformer in various noisy and reverberant conditions. Finally, we introduce a new adaptive beamformer, which combines the GSC with multichannel speech presence probability estimation and achieves superior source separation performance in noisy environment.