  In this paper, we address the problem of multichannel speech enhancement in
the short-time Fourier transform (STFT) domain. A long short-time memory (LSTM)
network takes as input a sequence of STFT coefficients associated with a
frequency bin of multichannel noisy-speech signals. The network's output is the
corresponding sequence of single-channel cleaned speech. We propose several
clean-speech network targets, namely, the magnitude ratio mask, the complex
STFT coefficients and the (smoothed) spatial filter. A prominent feature of the
proposed model is that the same LSTM architecture, with identical parameters,
is trained across frequency bins. The proposed method is referred to as
narrow-band deep filtering. This choice stays in contrast with traditional
wide-band speech enhancement methods. The proposed deep filtering is able to
discriminate between speech and noise by exploiting their different temporal
and spatial characteristics: speech is non-stationary and spatially coherent
while noise is relatively stationary and weakly correlated across channels.
This is similar in spirit with unsupervised techniques, such as spectral
subtraction and beamforming. We describe extensive experiments with both mixed
signals (noise is added to clean speech) and real signals (live recordings). We
empirically evaluate the proposed architecture variants using speech
enhancement and speech recognition metrics, and we compare our results with the
results obtained with several state of the art methods. In the light of these
experiments we conclude that narrow-band deep filtering has very good speech
enhancement and speech recognition performance, and excellent generalization
capabilities in terms of speaker variability and noise type.
