Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves

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dc.contributor.authorVan Truong, Hoangen
dc.contributor.authorTran, Khanh-Tungen
dc.contributor.authorVu, Xuan-Sonen
dc.contributor.authorNguyen, Duy-Khuongen
dc.contributor.authorBhuyan, Monowaren
dc.contributor.authorNguyen, Hoang D.en
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2024-09-09T08:43:25Z
dc.date.available2024-09-09T08:43:25Z
dc.date.issued2024-09-03en
dc.description.abstractThis paper investigates a novel graph-based representation of sound waves inspired by the physical phenomenon of correlated vibrations. We propose a Wave2Graph framework for integrating multiple acoustic representations, including the spectrum of frequencies and correlations, into various neural computing architectures to achieve new state-of-the-art performances in sound classification. The capability and reliability of our end-to-end framework are evidently demonstrated in voice pathology for low-cost and non-invasive mass-screening of medical conditions, including respiratory illnesses and Alzheimer’s Dementia. We conduct extensive experiments on multiple public benchmark datasets (ICBHI and ADReSSo) and our real-world dataset (IJSound: Respiratory disease detection using coughs and breaths). Wave2Graph framework consistently outperforms previous state-of-the-art methods with a large magnitude, up to 7.65% improvement, promising the usefulness of graph-based representation in signal processing and machine learning.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHoang, V.-T., Tran, K.-T., Vu, X.-S., Nguyen, D.-K., Bhuyan, M. and Nguyen, H.D. (2024) ‘Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves’, AI Open, 5, pp. 115–125. Available at: https://doi.org/10.1016/j.aiopen.2024.08.004en
dc.identifier.doihttps://doi.org/10.1016/j.aiopen.2024.08.004en
dc.identifier.endpage125en
dc.identifier.issn2666-6510en
dc.identifier.journaltitleAI Openen
dc.identifier.startpage115en
dc.identifier.urihttps://hdl.handle.net/10468/16306
dc.identifier.volume5en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartofAI Openen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Centres for Research Training Programme::Data and ICT Skills for the Future/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.rights© 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectWave2Graphen
dc.subjectGraph neural networken
dc.subjectCorrelationen
dc.subjectSound signal processingen
dc.subjectNeural network architectureen
dc.titleWave2Graph: Integrating spectral features and correlations for graph-based learning in sound wavesen
dc.typeArticle (peer-reviewed)en
dc.typejournal-articleen
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