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Clustering pathologic voice with kohonen SOM and hierarchical clustering

dc.contributor.authorTeixeira, João Paulo
dc.contributor.authorDajer, Maria
dc.contributor.authorOliveira, Alessa Anjos de
dc.date.accessioned2022-01-17T11:48:09Z
dc.date.available2022-01-17T11:48:09Z
dc.date.issued2021
dc.description.abstractThe main purpose of clustering voice pathologies is the attempt to form large groups of subjects with similar pathologies to be used with Deep-Learning. This paper focuses on applying Kohonen's Self-Organizing Maps and Hierarchical Clustering to investigate how these methods behave in the clustering procedure of voice samples by means of the parameters absolute jitter, relative jitter, absolute shimmer, relative shimmer, HNR, NHR and Autocorrelation. For this, a comparison is made between the speech samples of the Control group of subjects, the Hyper-functional Dysphonia and Vocal Folds Paralysis pathologies groups of subjects. As a result, the dataset was divided in two clusters, with no distinction between the pre-defined groups of pathologies. The result is aligned with previous result using statistical analysis.pt_PT
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationTeixeira, João Paulo; Dajer, Maria; Oliveira, Alessa de (2021). Clustering pathologic voice with kohonen SOM and hierarchical clustering. In 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC), 14th Int Conf on Bio-inspired Systems and Signal Processing (BIOSIGNALS), 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES). p. 158-163. ISBN 978-989-758-490-9pt_PT
dc.identifier.isbn978-989-758-490-9
dc.identifier.urihttp://hdl.handle.net/10198/24674
dc.language.isoporpt_PT
dc.peerreviewedyespt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAcoustic parameterspt_PT
dc.subjectClusteringpt_PT
dc.subjectHierarchical clusteringpt_PT
dc.subjectKohonen's self-organizing mapspt_PT
dc.subjectUnsupervised artificial neural networkspt_PT
dc.subjectVoice pathologiespt_PT
dc.titleClustering pathologic voice with kohonen SOM and hierarchical clusteringpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.endPage163pt_PT
oaire.citation.startPage158pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameTeixeira
person.givenNameJoão Paulo
person.identifier663194
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57069567500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscovery33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isProjectOfPublication6e01ddc8-6a82-4131-bca6-84789fa234bd
relation.isProjectOfPublication.latestForDiscovery6e01ddc8-6a82-4131-bca6-84789fa234bd

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