  Developing microphone array technologies for a small number of microphones is
important due to the constraints of many devices. One direction to address this
situation consists of virtually augmenting the number of microphone signals,
e.g., based on several physical model assumptions. However, such assumptions
are not necessarily met in realistic conditions. In this paper, as an
alternative approach, we propose a neural network-based virtual microphone
estimator (NN-VME). The NN-VME estimates virtual microphone signals directly in
the time domain, by utilizing the precise estimation capability of the recent
time-domain neural networks. We adopt a fully supervised learning framework
that uses actual observations at the locations of the virtual microphones at
training time. Consequently, the NN-VME can be trained using only multi-channel
observations and thus directly on real recordings, avoiding the need for
unrealistic physical model-based assumptions. Experiments on the CHiME-4 corpus
show that the proposed NN-VME achieves high virtual microphone estimation
performance even for real recordings and that a beamformer augmented with the
NN-VME improves both the speech enhancement and recognition performance.
