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'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
This paper outlines the development of a crosscorrelation
algorithm and a spiking neural network (SNN) for
sound localisation based on real sound recorded in a noisy and
dynamic environment by a mobile robot. The SNN architecture
aims to simulate the sound localisation ability of the
mammalian auditory pathways by exploiting the binaural cue
of interaural time difference (ITD). The medial superior olive
was the inspiration for the SNN architecture which required
the integration of an encoding layer which produced
biologically realistic spike trains, a model of the bushy cells
found in the cochlear nucleus and a supervised learning
algorithm. The experimental results demonstrate that
biologically inspired sound localisation achieved using a SNN
can compare favourably to the more classical technique of
cross-correlation
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