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Speech enhancement and source localization has been active research forseveral decades with a wide range of real-world applications. Recently, theDeep Complex Convolution Recurrent network (DCCRN) has yielded impressiveenhancement performance for single-channel systems. In this study, a neuralbeamformer consisting of a beamformer and a novel multi-channel DCCRN isproposed for speech enhancement and source localization. Complex-valued filtersestimated by the multi-channel DCCRN serve as the weights of beamformer. Inaddition, a one-stage learning-based procedure is employed for speechenhancement and source localization. The proposed network composed of themulti-channel DCCRN and the auxiliary network models the sound field, whileminimizing the distortionless response loss function. Simulation results showthat the proposed neural beamformer is effective in enhancing speech signals,with speech quality well preserved. The proposed neural beamformer alsoprovides source localization and voice activity detection (VAD) functions.

Multi-channel end-to-end neural network for speech enhancement, source localization, and voice activity detection