Biomimetic Binaural Sound Source Localisation with Ego-Noise Cancellation

Jorge Dávila-Chacón, Stefan Heinrich, Jingdong Liu, Stefan Wermter

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

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°.
Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Artificial Neural Networks (ICANN 2012)
EditorsAlessandro E.P. Villa, Włodzisław Duch, Péter Érdi, Francesco Masulli, Günther Palm
Number of pages8
Volume7552
PublisherSpringer
Publication date1 Sept 2012
Pages239-246
DOIs
Publication statusPublished - 1 Sept 2012
Externally publishedYes
SeriesLecture Notes in Computer Science

Keywords

  • Binaural sound source localisation
  • Spiking neural networks
  • Bayesian inference
  • Inferior colliculus

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