A binaural heterophasic adaptive beamformer and its deep learning assisted implementation

Beamforming is one of the most effective approaches to distant sound acquisition in complex acoustic environments, where noise, reverberation, and interference coexist; as a result, a significant number of efforts have been devoted to it over the last few decades. However, conventional beamformers produce a monaural output or colinear outputs, which are not optimal from the perception perspective. To take advantage of the human binaural hearing properties, a new type of fixed beamforming methods were developed recently, which attempt not only to attenuate noise but also render the signal of interest and residual noise into different perceptual regions, thereby achieving higher speech intelligibility. This work extends the principle of fixed binaural beamforming and develops a binaural heterophasic minimum variance distortionless response (MVDR) beamformer. A deep neural network (DNN) based noise estimation method is used to assist the implementation of this heterophasic MVDR beamformer, which is advantageous over the traditional one as it renders the desired source signal and residual noise to different perceptual regions, thereby yielding higher intelligibility. In comparison with the fixed binaural heterophasic beamformers, it can take advantage of the statistics of the noise to achieve better array performance. Results of simulations and listening tests validate the properties of the proposed technique.