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Recently, stunning improvements on multi-channel speech separation have beenachieved by neural beamformers when direction information is available.However, most of them neglect to utilize speaker's 2-dimensional (2D) locationcues contained in mixture signal, which limits the performance when two sourcescome from close directions. In this paper, we propose an end-to-end beamformingnetwork for 2D location guided speech separation merely given mixture signal.It first estimates discriminable direction and 2D location cues, which implydirections the sources come from in multi views of microphones and their 2Dcoordinates. These cues are then integrated into location-aware neuralbeamformer, thus allowing accurate reconstruction of two sources' speechsignals. Experiments show that our proposed model not only achieves acomprehensive decent improvement compared to baseline systems, but avoidsinferior performance on spatial overlapping cases.

Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation