Biomimetic binaural sound source localisation with ego-noise cancellation

Authors:
Jorge Dávila-Chacón;Stefan Heinrich;Jindong Liu;Stefan Wermter
Affiliations:
Department of Informatics, Knowledge Technology Group, University of Hamburg, Hamburg, Germany;Department of Informatics, Knowledge Technology Group, University of Hamburg, Hamburg, Germany;Department of Computing, Imperial College London, London, UK;Department of Informatics, Knowledge Technology Group, University of Hamburg, Hamburg, Germany
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 3
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Abstract

This paper presents a spiking neural network (SNN) for binaural sound source localisation (SSL). The cues used for SSL were the interaural time (ITD) and level (ILD) differences. ITDs and ILDs were extracted with models of the medial superior olive (MSO) and the lateral superior olive (LSO). The MSO and LSO outputs were integrated in a model of the inferior colliculus (IC). The connection weights between the MSO and LSO neurons to the IC neurons were estimated using Bayesian inference. This inference process allowed the algorithm to perform robustly on a robot with ~40,dB of ego-noise. The results showed that the algorithm is capable of differentiating sounds with an accuracy of 15°.