  In this contribution, we present a novel online approach to multichannel
speech enhancement. The proposed method estimates the enhanced signal through a
filter-and-sum framework. More specifically, complex-valued masks are estimated
by a deep complex-valued neural network, termed the complex-valued spatial
autoencoder. The proposed network is capable of exploiting as well as
manipulating both the phase and the amplitude of the microphone signals. As
shown by the experimental results, the proposed approach is able to exploit
both spatial and spectral characteristics of the desired source signal
resulting in a physically plausible spatial selectivity and superior speech
quality compared to other baseline methods.
