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Recent research advances in deep neural network (DNN)-based beamformers haveshown great promise for speech enhancement under adverse acoustic conditions.Different network architectures and input features have been explored inestimating beamforming weights. In this paper, we propose a deep beamformerbased on an efficient convolutional recurrent network (CRN) trained with anovel ARray RespOnse-aWare (ARROW) loss function. The ARROW loss exploits thearray responses of the target and interferer by using the ground truth relativetransfer functions (RTFs). The DNN-based beamforming system, trained with ARROWloss through supervised learning, is able to perform speech enhancement andspeaker localization jointly. Experimental results have shown that the proposeddeep beamformer, trained with the linearly weighted scale-invariantsource-to-noise ratio (SI-SNR) and ARROW loss functions, achieves superiorperformance in speech enhancement and speaker localization compared to twobaselines.

Deep Beamforming for Speech Enhancement and Speaker Localization with an Array Response-Aware Loss Function