A spiking neural network approach to auditory source lateralisation

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    Abstract

    A novel approach to multi-microphone acoustic source localisation based on spiking neural networks is presented. We demonstrate that a two microphone system connected to a spiking neural network can be used to localise acoustic sources based purely on inter microphone timing differences, with no need for manually configured delay lines. A two sensor example is provided which includes 1) a front end which converts the acoustic signal to a series of spikes, 2) a hidden layer of spiking neurons, 3) an output layer of spiking neurons which represents the location of the acoustic source. We present details on training the network, and evaluation of its performance in quiet and noisy conditions. The system is trained on two locations, and we show that the lateralisation accuracy is 100% when presented with previously unseen data in quiet conditions. We also demonstrate the network generalises to modulation rates and background noise on which it was not trained.

    Original languageEnglish
    Title of host publicationICASSP 2019: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing
    Subtitle of host publicationproceedings
    Place of PublicationPiscataway, NJ
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages1488-1492
    Number of pages5
    ISBN (Electronic)9781479981311
    DOIs
    Publication statusPublished - 2019
    Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
    Duration: 12 May 201917 May 2019

    Conference

    Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
    CountryUnited Kingdom
    CityBrighton
    Period12/05/1917/05/19

    Keywords

    • spiking neural networks
    • binaural localisation algorithms
    • acoustic source localisation
    • machine learning

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