  A novel end-to-end binaural sound localisation approach is proposed which
estimates the azimuth of a sound source directly from the waveform. Instead of
employing hand-crafted features commonly employed for binaural sound
localisation, such as the interaural time and level difference, our end-to-end
system approach uses a convolutional neural network (CNN) to extract specific
features from the waveform that are suitable for localisation. Two systems are
proposed which differ in the initial frequency analysis stage. The first system
is auditory-inspired and makes use of a gammatone filtering layer, while the
second system is fully data-driven and exploits a trainable convolutional layer
to perform frequency analysis. In both systems, a set of dedicated
convolutional kernels are then employed to search for specific localisation
cues, which are coupled with a localisation stage using fully connected layers.
Localisation experiments using binaural simulation in both anechoic and
reverberant environments show that the proposed systems outperform a
state-of-the-art deep neural network system. Furthermore, our investigation of
the frequency analysis stage in the second system suggests that the CNN is able
to exploit different frequency bands for localisation according to the
characteristics of the reverberant environment.
