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Classification of control/pathologic subjects with support vector machines

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Resumo:The diagnosis of pathologies using vocal acoustic analysis has the advantage of been noninvasive and inexpensive technique compared to traditional technique in use. In this work the SVM were experimentally tested to diagnose dysphonia, chronic laryngitis or vocal cords paralysis. Three groups of parameters were experimented. Jitter, shimmer and HNR, MFCCs extracted from a sustained vowels and MFCC extracted from a short sentence. The first group showed their importance in this type of diagnose and the second group showed low discriminative power. The SVM functions and methods were also experimented using the dataset with and without gender separation. The best accuracy was 71% using the jitter, shimmer and HNR parameters without gender separation.
Autores principais:Teixeira, Felipe
Outros Autores:Fernandes, Joana Filipa Teixeira; Guedes, Victor; Candido Junior, Arnaldo; Teixeira, João Paulo
Assunto:Vocal acoustic analysis MFCCs Jitter Shimmer HNR SVM functions SVM methods
Ano:2018
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB
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author Teixeira, Felipe
author2 Fernandes, Joana Filipa Teixeira
Guedes, Victor
Candido Junior, Arnaldo
Teixeira, João Paulo
author2_role author
author
author
author
author_facet Teixeira, Felipe
Fernandes, Joana Filipa Teixeira
Guedes, Victor
Candido Junior, Arnaldo
Teixeira, João Paulo
author_role author
contributor_name_str_mv Biblioteca Digital do IPB
country_str PT
creators_json_str [{\"Person.name\":\"Teixeira, Felipe\"},{\"Person.name\":\"Fernandes, Joana Filipa Teixeira\",\"Person.identifier.orcid\":\"0000-0002-0618-4627\"},{\"Person.name\":\"Guedes, Victor\"},{\"Person.name\":\"Candido Junior, Arnaldo\"},{\"Person.name\":\"Teixeira, João Paulo\",\"Person.identifier.orcid\":\"0000-0002-6679-5702\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Biblioteca Digital do IPB
datacite.creators.creator.creatorName.fl_str_mv Teixeira, Felipe
Fernandes, Joana Filipa Teixeira
Guedes, Victor
Candido Junior, Arnaldo
Teixeira, João Paulo
datacite.date.Accepted.fl_str_mv 2018-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2020-04-23T09:00:25Z
datacite.date.embargoed.fl_str_mv 2020-04-23T09:00:25Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv Vocal acoustic analysis
MFCCs
Jitter
Shimmer
HNR
SVM functions
SVM methods
datacite.titles.title.fl_str_mv Classification of control/pathologic subjects with support vector machines
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.creator.none.fl_str_mv Teixeira, Felipe
Fernandes, Joana Filipa Teixeira
Guedes, Victor
Candido Junior, Arnaldo
Teixeira, João Paulo
dc.date.Accepted.fl_str_mv 2018-01-01T00:00:00Z
dc.date.available.fl_str_mv 2020-04-23T09:00:25Z
dc.date.embargoed.fl_str_mv 2020-04-23T09:00:25Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10198/21791
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv Elsevier
dc.rights.cclincense.fl_str_mv http://creativecommons.org/licenses/by/4.0/
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv Vocal acoustic analysis
MFCCs
Jitter
Shimmer
HNR
SVM functions
SVM methods
dc.title.fl_str_mv Classification of control/pathologic subjects with support vector machines
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_5794
description The diagnosis of pathologies using vocal acoustic analysis has the advantage of been noninvasive and inexpensive technique compared to traditional technique in use. In this work the SVM were experimentally tested to diagnose dysphonia, chronic laryngitis or vocal cords paralysis. Three groups of parameters were experimented. Jitter, shimmer and HNR, MFCCs extracted from a sustained vowels and MFCC extracted from a short sentence. The first group showed their importance in this type of diagnose and the second group showed low discriminative power. The SVM functions and methods were also experimented using the dataset with and without gender separation. The best accuracy was 71% using the jitter, shimmer and HNR parameters without gender separation.
dirty 0
eu_rights_str_mv openAccess
format conferencePaper
fulltext.url.fl_str_mv https://bibliotecadigital.ipb.pt/bitstreams/a30ceb6a-8628-4448-b86a-7c59cf70f51b/download
id ipb_bbe46b869f0183986d35da16979f66b7
identifier.url.fl_str_mv http://hdl.handle.net/10198/21791
instacron_str ipb
institution Instituto Politécnico de Bragança
instname_str Instituto Politécnico de Bragança
language eng
network_acronym_str ipb
network_name_str Biblioteca Digital do IPB
oai_identifier_str oai:bibliotecadigital.ipb.pt:10198/21791
organization_str_mv urn:organizationAcronym:ipb
person_str_mv Teixeira, Felipe
Teixeira, Felipe
https://www.ciencia-id.pt/0E17-62FB-AA17
0E17-62FB-AA17
Fernandes, Joana Filipa Teixeira
Fernandes, Joana Filipa Teixeira
https://www.ciencia-id.pt/AE12-440A-299D
AE12-440A-299D
http://orcid.org/0000-0002-0618-4627
0000-0002-0618-4627
Guedes, Victor
Candido Junior, Arnaldo
Teixeira, João Paulo
Teixeira, João Paulo
https://www.ciencia-id.pt/4F15-B322-59B4
4F15-B322-59B4
http://orcid.org/0000-0002-6679-5702
0000-0002-6679-5702
publishDate 2018
publisher.none.fl_str_mv Elsevier
reponame_str Biblioteca Digital do IPB
repository_id_str urn:repositoryAcronym:ipb
service_str_mv urn:repositoryAcronym:ipb
spelling engElsevierpt_PTThe diagnosis of pathologies using vocal acoustic analysis has the advantage of been noninvasive and inexpensive technique compared to traditional technique in use. In this work the SVM were experimentally tested to diagnose dysphonia, chronic laryngitis or vocal cords paralysis. Three groups of parameters were experimented. Jitter, shimmer and HNR, MFCCs extracted from a sustained vowels and MFCC extracted from a short sentence. The first group showed their importance in this type of diagnose and the second group showed low discriminative power. The SVM functions and methods were also experimented using the dataset with and without gender separation. The best accuracy was 71% using the jitter, shimmer and HNR parameters without gender separation.application/pdfpt_PTClassification of control/pathologic subjects with support vector machinesPersonalTeixeira, FelipeDSpacehttp://dspace.org/items/764c5209-b9ab-479e-b5be-59fbe07c784bDSpacehttp://dspace.org/items/764c5209-b9ab-479e-b5be-59fbe07c784bTeixeiraFelipeCiência IDhttps://www.ciencia-id.pt0E17-62FB-AA17PersonalFernandes, Joana Filipa TeixeiraDSpacehttp://dspace.org/items/a6f7a119-fbc9-439f-8dd9-0bbc9ec82fadDSpacehttp://dspace.org/items/a6f7a119-fbc9-439f-8dd9-0bbc9ec82fadFernandesJoana Filipa TeixeiraCiência IDhttps://www.ciencia-id.ptAE12-440A-299DORCIDhttp://orcid.org0000-0002-0618-4627Guedes, VictorCandido Junior, ArnaldoPersonalTeixeira, João PauloDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfTeixeiraJoão PauloCiência IDhttps://www.ciencia-id.pt4F15-B322-59B4ORCIDhttp://orcid.org0000-0002-6679-5702Researcher IDhttps://www.researcherid.comN-6576-2013Scopus Author IDhttps://www.scopus.com57069567500HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptDOIIsPartOf10.1016/j.procs.2018.10.0392020-04-23T09:00:25Z20182018-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/21791http://purl.org/coar/access_right/c_abf2open accessVocal acoustic analysisMFCCsJitterShimmerHNRSVM functionsSVM methods496242 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paper2018http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/a30ceb6a-8628-4448-b86a-7c59cf70f51b/downloadInternational Conference on ENTERprise Information Systems / International Conference on Project MANagement138272279Lisbon; Portugal
spellingShingle Classification of control/pathologic subjects with support vector machines
Teixeira, Felipe
Vocal acoustic analysis
MFCCs
Jitter
Shimmer
HNR
SVM functions
SVM methods
subject.fl_str_mv Vocal acoustic analysis
MFCCs
Jitter
Shimmer
HNR
SVM functions
SVM methods
title Classification of control/pathologic subjects with support vector machines
title_full Classification of control/pathologic subjects with support vector machines
title_fullStr Classification of control/pathologic subjects with support vector machines
title_full_unstemmed Classification of control/pathologic subjects with support vector machines
title_short Classification of control/pathologic subjects with support vector machines
title_sort Classification of control/pathologic subjects with support vector machines
topic Vocal acoustic analysis
MFCCs
Jitter
Shimmer
HNR
SVM functions
SVM methods
topic_facet Vocal acoustic analysis
MFCCs
Jitter
Shimmer
HNR
SVM functions
SVM methods
url http://hdl.handle.net/10198/21791
visible 1