  Speech enhancement and source localization has been active research for
several decades with a wide range of real-world applications. Recently, the
Deep Complex Convolution Recurrent network (DCCRN) has yielded impressive
enhancement performance for single-channel systems. In this study, a neural
beamformer consisting of a beamformer and a novel multi-channel DCCRN is
proposed for speech enhancement and source localization. Complex-valued filters
estimated by the multi-channel DCCRN serve as the weights of beamformer. In
addition, a one-stage learning-based procedure is employed for speech
enhancement and source localization. The proposed network composed of the
multi-channel DCCRN and the auxiliary network models the sound field, while
minimizing the distortionless response loss function. Simulation results show
that the proposed neural beamformer is effective in enhancing speech signals,
with speech quality well preserved. The proposed neural beamformer also
provides source localization and voice activity detection (VAD) functions.
