A biomimetic spiking neural network of the auditory midbrain for mobile robot sound localisation in reverberant environments

  • Authors:

  • Jindong Liu;David Perez-Gonzalez;Adrian Rees;Harry Erwin;Stefan Wermter

  • Affiliations:

  • Faculty of School of Computing and Technology, University of Sunderland, Sunderland, United Kingdom;Institute of Neuroscience, The Medical School, Newcastle University, United Kingdom;Institute of Neuroscience, The Medical School, Newcastle University, United Kingdom;Faculty of School of Computing and Technology, University of Sunderland, Sunderland, United Kingdom;Faculty of School of Computing and Technology, University of Sunderland, Sunderland, United Kingdom

  • Venue:
  • IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper proposes a spiking neural network (SNN) of the mammalian auditory midbrain to achieve binaural sound source localisation with a mobile robot. The network is inspired by neurophysiological studies on the organisation of binaural processing in the medial superior olive (MSO), lateral superior olive (LSO) and the inferior colliculus (IC) to achieve a sharp azimuthal localisation of sound source over a wide frequency range in situations where there is auditory clutter and reverberation. Three groups of artificial neurons are constructed to represent the neurons in the MSO, LSO and IC that are sensitive to interaural time difference (lTD), interaural level difference (ILD) and azimuth angle respectively. The lTD and ILD cues are combined in the IC using Bayes's theorem to estimate the azimuthal direction of a sound source. Two of known IC cells, onset and sustainedregular are modelled. The azimuth estimations at different robot positions are then used to calculate the sound source position by a triangulation method using an environment map constructed by a laser scanner. The experimental results show that the addition of ILD information significantly increases sound localisation performance at frequencies above 1 kHz. The mobile robot is able to localise a sound source in an acoustically cluttered and reverberant environment.