  This study proposes a multi-microphone complex spectral mapping approach for
speech dereverberation on a fixed array geometry. In the proposed approach, a
deep neural network (DNN) is trained to predict the real and imaginary (RI)
components of direct sound from the stacked reverberant (and noisy) RI
components of multiple microphones. We also investigate the integration of
multi-microphone complex spectral mapping with beamforming and post-filtering.
Experimental results on multi-channel speech dereverberation demonstrate the
effectiveness of the proposed approach.
