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Vocal acoustic analysis – classification of dysphonic voices with artificial neural network

dc.contributor.authorTeixeira, João Paulo
dc.contributor.authorFernandes, Paula Odete
dc.contributor.authorAlves, Nuno Filipe Ribeiro
dc.date.accessioned2018-05-03T09:47:21Z
dc.date.available2018-05-03T09:47:21Z
dc.date.issued2017
dc.description.abstractVoice acoustic analysis is becoming nowadays a useful tool for detection of laryngological pathologies. This techniques enables a non-invasive and low cost assessment of voice disorders allowing a more efficient fast and objective diagnosis, permitting the patients to get a suitable treatment. In this work, the best predictors/parameters for diagnose of dysphonia were experimented. A vector made up of 4 Jitter parameters, 4 Shimmer parameters and Harmonic to Noise Ratio (HNR), determined from 3 different vowels at 3 different tones, in a total of 81 features, was used. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. For the classification models based on artificial neural network (ANN) was used. The methods/models found allowed us to obtain an Accuracy of 100% for female voices and 90% for male voices using only Jitter Shimmer and HNR parameterspt_PT
dc.description.sponsorshipThis work is supported by the Fundação para a Ciência e Tecnologia (FCT) under the projects number UID/GES/4752/2016 and UID/GES/04630/2013.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationTeixeira, João Paulo; Fernandes, Paula O.; Alves, Nuno (2017). Vocal acoustic analysis – classification of dysphonic voices with artificial neural networks. In International Conference on ENTERprise Information Systems, CENTERIS 2017, International Conference on Project MANagement, ProjMAN 2017 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2017. Barcelona. p. 9-26.pt_PT
dc.identifier.doi10.1016/j.procs.2017.11.004pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/17578
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationUnidade de Investigação Aplicada em Gestão
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectVoice analysispt_PT
dc.subjectDysphonypt_PT
dc.subjectANNpt_PT
dc.subjectPCApt_PT
dc.subjectMultilinear regression analysispt_PT
dc.subjectHierarquical clusteringpt_PT
dc.titleVocal acoustic analysis – classification of dysphonic voices with artificial neural networkpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleUnidade de Investigação Aplicada em Gestão
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FGES%2F4752%2F2016/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FGES%2F04630%2F2013/PT
oaire.citation.endPage26pt_PT
oaire.citation.startPage19pt_PT
oaire.citation.titleInternational Conference on ENTERprise Information Systems, CENTERIS 2017, International Conference on Project MANagement, ProjMAN 2017 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2017; Barcelona; Spain;pt_PT
oaire.citation.volume121pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream5876
person.familyNameTeixeira
person.familyNameFernandes
person.givenNameJoão
person.givenNamePaula Odete
person.identifier663194
person.identifierN-3804-2013
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.ciencia-id991D-9D1E-D67D
person.identifier.orcid0000-0002-6679-5702
person.identifier.orcid0000-0001-8714-4901
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57069567500
person.identifier.scopus-author-id35200741800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication2269147c-2b53-4d1c-bc1b-f1367d197262
relation.isAuthorOfPublication.latestForDiscovery33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
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