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Wall, Julie; McGinnity, Thomas M.; Maguire, Liam P. (2011)
Publisher: IEEE
Languages: English
Types: Unknown
This paper outlines the development of a crosscorrelation\ud algorithm and a spiking neural network (SNN) for\ud sound localisation based on real sound recorded in a noisy and\ud dynamic environment by a mobile robot. The SNN architecture\ud aims to simulate the sound localisation ability of the\ud mammalian auditory pathways by exploiting the binaural cue\ud of interaural time difference (ITD). The medial superior olive\ud was the inspiration for the SNN architecture which required\ud the integration of an encoding layer which produced\ud biologically realistic spike trains, a model of the bushy cells\ud found in the cochlear nucleus and a supervised learning\ud algorithm. The experimental results demonstrate that\ud biologically inspired sound localisation achieved using a SNN\ud can compare favourably to the more classical technique of\ud cross-correlation.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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