  This paper addresses the problem of microphone array generalization for
deep-learning-based end-to-end multichannel speech enhancement. We aim to train
a unique deep neural network (DNN) potentially performing well on unseen
microphone arrays. The microphone array geometry shapes the network's
parameters when training on a fixed microphone array, and thus restricts the
generalization of the trained network to another microphone array. To resolve
this problem, a single network is trained using data recorded by various
microphone arrays of different geometries. We design three variants of our
recently proposed narrowband network to cope with the agnostic number of
microphones. Overall, the goal is to make the network learn the universal
information for speech enhancement that is available for any array geometry,
rather than learn the one-array-dedicated characteristics. The experiments on
both simulated and real room impulse responses (RIR) demonstrate the excellent
across-array generalization capability of the proposed networks, in the sense
that their performance measures are very close to, or even exceed the network
trained with test arrays. Moreover, they notably outperform various beamforming
methods and other advanced deep-learning-based methods.
