  Building on the deep learning based acoustic echo cancellation (AEC) in the
single-loudspeaker (single-channel) and single-microphone setup, this paper
investigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC). We
train a deep neural network (DNN) to predict the near-end speech from
microphone signals with far-end signals used as additional information. We find
that the deep learning approach avoids the non-uniqueness problem in
traditional MCAEC algorithms. For the AEC setup with multiple microphones,
rather than employing AEC for each microphone, a single DNN is trained to
achieve echo removal for all microphones. Also, combining deep learning based
AEC with deep learning based beamforming further improves the system
performance. Experimental results show the effectiveness of both bidirectional
long short-term memory (BLSTM) and convolutional recurrent network (CRN) based
methods for MCAEC and MMAEC. Furthermore, deep learning based methods are
capable of removing echo and noise simultaneously and work well in the presence
of nonlinear distortions.
