  In a multi-channel separation task with multiple speakers, we aim to recover
all individual speech signals from the mixture. In contrast to single-channel
approaches, which rely on the different spectro-temporal characteristics of the
speech signals, multi-channel approaches should additionally utilize the
different spatial locations of the sources for a more powerful separation
especially when the number of sources increases. To enhance the spatial
processing in a multi-channel source separation scenario, in this work, we
propose a deep neural network (DNN) based spatially selective filter (SSF) that
can be spatially steered to extract the speaker of interest by initializing a
recurrent neural network layer with the target direction. We compare the
proposed SSF with a common end-to-end direct separation (DS) approach trained
using utterance-wise permutation invariant training (PIT), which only
implicitly learns to perform spatial filtering. We show that the SSF has a
clear advantage over a DS approach with the same underlying network
architecture when there are more than two speakers in the mixture, which can be
attributed to a better use of the spatial information. Furthermore, we find
that the SSF generalizes much better to additional noise sources that were not
seen during training and to scenarios with speakers positioned at a similar
angle.
