  This paper presents a novel machine-hearing system that exploits deep neural
networks (DNNs) and head movements for robust binaural localisation of multiple
sources in reverberant environments. DNNs are used to learn the relationship
between the source azimuth and binaural cues, consisting of the complete
cross-correlation function (CCF) and interaural level differences (ILDs). In
contrast to many previous binaural hearing systems, the proposed approach is
not restricted to localisation of sound sources in the frontal hemifield. Due
to the similarity of binaural cues in the frontal and rear hemifields,
front-back confusions often occur. To address this, a head movement strategy is
incorporated in the localisation model to help reduce the front-back errors.
The proposed DNN system is compared to a Gaussian mixture model (GMM) based
system that employs interaural time differences (ITDs) and ILDs as localisation
features. Our experiments show that the DNN is able to exploit information in
the CCF that is not available in the ITD cue, which together with head
movements substantially improves localisation accuracies under challenging
acoustic scenarios in which multiple talkers and room reverberation are
present.
