Dynamic- Structured Reservoir Spiking Neural Network in Sound Localization

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aut.relation.endpage1
aut.relation.journalIEEE Access
aut.relation.startpage1
dc.contributor.authorRoozbehi, Zahra
dc.contributor.authorNarayanan, Ajit
dc.contributor.authorMohaghegh, Mahsa
dc.contributor.authorSaeedinia, Samaneh-Alsadat
dc.date.accessioned2024-02-07T02:42:31Z
dc.date.available2024-02-07T02:42:31Z
dc.date.issued2024
dc.description.abstractSound source localization is a critical problem in various fields, including communication, security, and entertainment. Binaural cues are a natural technique used by mammalian ears for efficient sound source localization. Spiking neural networks (SNNs) have emerged as a promising tool for implementing binaural sound source localization approaches. However, optimizing the topology and size of SNNs is crucial to reduce computational costs while maintaining accuracy. This paper proposes a real-time structure of a reservoir SNN (rSNN) called Adaptive-Resonance-Theory-based rSNN (ART-rSNN) for localizing sound sources in the time domain by integrating an energy-based localization method. The dataset used in this work is recorded by two different omnidirectional microphones from a real environment. The dataset includes various sound events such as speech, music, and environmental sounds. The proposed ART-rSNN architecture can dynamically adjust the location of its neurons to amplify estimated energy near the sound source, resulting in higher localization accuracy. Our proposed method outperforms several conventional and state of the art algorithms in terms of accuracy and is able to detect the front and back direction of azimuth angle. This work demonstrates the potential of dynamic neuron arrangements in SNNs for improving sound source localization in practical applications.
dc.identifier.citationIEEE Access, ISSN: 2169-3536 (Online), Institute of Electrical and Electronics Engineers (IEEE), 1-1. doi: 10.1109/access.2024.3360491
dc.identifier.doi10.1109/access.2024.3360491
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10292/17190
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urihttps://ieeexplore.ieee.org/document/10418107
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject08 Information and Computing Sciences
dc.subject09 Engineering
dc.subject10 Technology
dc.subject40 Engineering
dc.subject46 Information and computing sciences
dc.titleDynamic- Structured Reservoir Spiking Neural Network in Sound Localization
dc.typeJournal Article
pubs.elements-id537469
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