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Recently, many deep learning based beamformers have been proposed formulti-channel speech separation. Nevertheless, most of them rely on extra cuesknown in advance, such as speaker feature, face image or directionalinformation. In this paper, we propose an end-to-end beamforming network fordirection guided speech separation given merely the mixture signal, namelyMIMO-DBnet. Specifically, we design a multi-channel input and multiple outputsarchitecture to predict the direction-of-arrival based embeddings andbeamforming weights for each source. The precisely estimated directionalembedding provides quite effective spatial discrimination guidance for theneural beamformer to offset the effect of phase wrapping, thus allowing moreaccurate reconstruction of two sources' speech signals. Experiments show thatour proposed MIMO-DBnet not only achieves a comprehensive decent improvementcompared to baseline systems, but also maintain the performance on highfrequency bands when phase wrapping occurs.

MIMO-DBnet: Multi-channel Input and Multiple Outputs DOA-aware Beamforming Network for Speech Separation