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Building on the deep learning based acoustic echo cancellation (AEC) in thesingle-loudspeaker (single-channel) and single-microphone setup, this paperinvestigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC). Wetrain a deep neural network (DNN) to predict the near-end speech frommicrophone signals with far-end signals used as additional information. We findthat the deep learning approach avoids the non-uniqueness problem intraditional MCAEC algorithms. For the AEC setup with multiple microphones,rather than employing AEC for each microphone, a single DNN is trained toachieve echo removal for all microphones. Also, combining deep learning basedAEC with deep learning based beamforming further improves the systemperformance. Experimental results show the effectiveness of both bidirectionallong short-term memory (BLSTM) and convolutional recurrent network (CRN) basedmethods for MCAEC and MMAEC. Furthermore, deep learning based methods arecapable of removing echo and noise simultaneously and work well in the presenceof nonlinear distortions.

Multi-Channel and Multi-Microphone Acoustic Echo Cancellation Using A Deep Learning Based Approach