  Recent research advances in deep neural network (DNN)-based beamformers have
shown great promise for speech enhancement under adverse acoustic conditions.
Different network architectures and input features have been explored in
estimating beamforming weights. In this paper, we propose a deep beamformer
based on an efficient convolutional recurrent network (CRN) trained with a
novel ARray RespOnse-aWare (ARROW) loss function. The ARROW loss exploits the
array responses of the target and interferer by using the ground truth relative
transfer functions (RTFs). The DNN-based beamforming system, trained with ARROW
loss through supervised learning, is able to perform speech enhancement and
speaker localization jointly. Experimental results have shown that the proposed
deep beamformer, trained with the linearly weighted scale-invariant
source-to-noise ratio (SI-SNR) and ARROW loss functions, achieves superior
performance in speech enhancement and speaker localization compared to two
baselines.
