Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | spa |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/21790 |
Resumo: | Vocal acoustic analysis is becoming a useful tool for the classification and recognition of laryngological pathologies. This technique enables a non-invasive and low-cost assessment of voice disorders, allowing a more efficient, fast, and objective diagnosis. In this work, ANN and SVM were experimented on to classify between dysphonic/control and vocal cord paralysis/control. A vector was made up of 4 jitter parameters, 4 shimmer parameters, and a harmonic to noise ratio (HNR), determined from 3 different vowels at 3 different tones, with a total of 81 features. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. The classification between dysphonic and control was made with an accuracy of 100% for female and male groups with ANN and SVM. For the classification between vocal cords paralysis and control an accuracy of 78,9% was achieved for female group with SVM, and 81,8% for the male group with ANN. |
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Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysisANNClassificationFeature selectionHierarchical clusteringHNRJitterMultilinear regression analysisPCAShimmerSVMVocal acoustic analysisVoice pathologiesVocal acoustic analysis is becoming a useful tool for the classification and recognition of laryngological pathologies. This technique enables a non-invasive and low-cost assessment of voice disorders, allowing a more efficient, fast, and objective diagnosis. In this work, ANN and SVM were experimented on to classify between dysphonic/control and vocal cord paralysis/control. A vector was made up of 4 jitter parameters, 4 shimmer parameters, and a harmonic to noise ratio (HNR), determined from 3 different vowels at 3 different tones, with a total of 81 features. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. The classification between dysphonic and control was made with an accuracy of 100% for female and male groups with ANN and SVM. For the classification between vocal cords paralysis and control an accuracy of 78,9% was achieved for female group with SVM, and 81,8% for the male group with ANN.IGI GlobalBiblioteca Digital do IPBTeixeira, João PauloAlves, Nuno Filipe RibeiroFernandes, Paula Odete2020-04-23T08:54:43Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/21790spaTeixeira, João Paulo; Alves, Nuno; Fernandes, Paula O. (2020). Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis. International Journal of E-Health and Medical Communications (IJEHMC). ISSN 1947-315X. 11:1, p. 37-511947-315X10.4018/IJEHMC.20200101031947-3168info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-21T10:49:07Zoai:bibliotecadigital.ipb.pt:10198/21790Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:13:11.314078Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis |
title |
Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis |
spellingShingle |
Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis Teixeira, João Paulo ANN Classification Feature selection Hierarchical clustering HNR Jitter Multilinear regression analysis PCA Shimmer SVM Vocal acoustic analysis Voice pathologies |
title_short |
Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis |
title_full |
Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis |
title_fullStr |
Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis |
title_full_unstemmed |
Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis |
title_sort |
Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis |
author |
Teixeira, João Paulo |
author_facet |
Teixeira, João Paulo Alves, Nuno Filipe Ribeiro Fernandes, Paula Odete |
author_role |
author |
author2 |
Alves, Nuno Filipe Ribeiro Fernandes, Paula Odete |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Teixeira, João Paulo Alves, Nuno Filipe Ribeiro Fernandes, Paula Odete |
dc.subject.por.fl_str_mv |
ANN Classification Feature selection Hierarchical clustering HNR Jitter Multilinear regression analysis PCA Shimmer SVM Vocal acoustic analysis Voice pathologies |
topic |
ANN Classification Feature selection Hierarchical clustering HNR Jitter Multilinear regression analysis PCA Shimmer SVM Vocal acoustic analysis Voice pathologies |
description |
Vocal acoustic analysis is becoming a useful tool for the classification and recognition of laryngological pathologies. This technique enables a non-invasive and low-cost assessment of voice disorders, allowing a more efficient, fast, and objective diagnosis. In this work, ANN and SVM were experimented on to classify between dysphonic/control and vocal cord paralysis/control. A vector was made up of 4 jitter parameters, 4 shimmer parameters, and a harmonic to noise ratio (HNR), determined from 3 different vowels at 3 different tones, with a total of 81 features. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. The classification between dysphonic and control was made with an accuracy of 100% for female and male groups with ANN and SVM. For the classification between vocal cords paralysis and control an accuracy of 78,9% was achieved for female group with SVM, and 81,8% for the male group with ANN. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-04-23T08:54:43Z 2020 2020-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/21790 |
url |
http://hdl.handle.net/10198/21790 |
dc.language.iso.fl_str_mv |
spa |
language |
spa |
dc.relation.none.fl_str_mv |
Teixeira, João Paulo; Alves, Nuno; Fernandes, Paula O. (2020). Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis. International Journal of E-Health and Medical Communications (IJEHMC). ISSN 1947-315X. 11:1, p. 37-51 1947-315X 10.4018/IJEHMC.2020010103 1947-3168 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IGI Global |
publisher.none.fl_str_mv |
IGI Global |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799135407449833472 |