A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics

Conference paper

Wall, J., McGinnity, Thomas M. and Maguire, Liam P. 2011. A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics. Neural Networks (IJCNN), The 2011 International Joint Conference on. San Jose, CA 31 Jul - 05 Aug 2011 IEEE. pp. 1981-1987
AuthorsWall, J., McGinnity, Thomas M. and Maguire, Liam P.
TypeConference paper
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.

Year2011
ConferenceNeural Networks (IJCNN), The 2011 International Joint Conference on
PublisherIEEE
Accepted author manuscript
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CC BY-NC-ND
Publication dates
PrintJul 2011
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Deposited20 Jul 2016
Web address (URL)http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6033468
https://www.researchgate.net/publication/234128563_A_comparison_of_sound_localisation_techniques_using_cross-correlation_and_spiking_neural_networks_for_mobile_robotics
Additional information

© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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