Robust Minimum Variance Distortionless Response Beamformer based on Target Activity Detection in Binaural Hearing Aid Applications In this work, a new beamforming algorithm for binaural hearing aids is introduced, with a good robustness to errors in the estimated target direction of arrival. The proposed design is based on a selection between a monaural MVDR beamformer and a binaural MVDR beamformer at each timefrequency bin. The decision of using either the monaural MVDR or the binaural MVDR at each timefrequency bin depends on a proposed target activity detection system, over an a priori determined target zone. The proposed target activity detection system uses a binaural beamforming method in targetcancelling mode, in order to get an estimate of the dominant speaker direction at each timefrequency bin. Simulations are performed using signals and propagation models obtained from reallife multichannel binaural hearing aids, to evaluate the resulting noise reduction and target distortion. Simulation results show the robustness of the proposed design to errors in the estimated target direction of arrival, in comparison with both the monaural MVDR and binaural MVDR algorithms.

Robust Bayesian and Maximum a Posteriori Beamforming for Hearing Assistive Devices Multimicrophone speech enhancement systems often apply beamforming to enhance one or multiple desired signals in a noisy environment. Common for many beamforming methods, is that they require the directionofarrival (DOA) of the target sound source to be known in order to achieve optimal noise reduction performance. To improve robustness against DOA uncertainty, we propose maximum a posteriori (MAP) and Bayesian beamformers that are able to take advantage of prior information on the target direction. We compare the proposed MAP and Bayesian beamformers to stateoftheart beamforming methods for noise reduction in hearing assistive devices. We evaluate the proposed beamformers in isotropic babble noise in terms of segmental SNR (SSNR) and extended shorttime objective intelligibility (ESTOI). Results show that the proposed methods outperform current stateoftheart beamformers used for noise reduction in hearing aids in most scenarios.