Musical Feature Based Classification of Parkinson's Disease Using Dysphonic Speech

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dc.authoridUlukaya, Sezer/0000-0003-0473-7547
dc.authoridKurt, İlke/0000-0001-5911-9282;
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.authorwosidUlukaya, Sezer/HJY-5331-2023
dc.authorwosidKurt, İlke/AAG-6476-2019
dc.authorwosiderdem, oğuzhan/AAG-6229-2019
dc.contributor.authorKurt, Ilke
dc.contributor.authorUlukaya, Sezer
dc.contributor.authorErdem, Oguzhan
dc.date.accessioned2024-06-12T10:59:57Z
dc.date.available2024-06-12T10:59:57Z
dc.date.issued2018
dc.departmentTrakya Üniversitesien_US
dc.description41st International Conference on Telecommunications and Signal Processing (TSP) -- JUL 04-06, 2018 -- Athens, GREECEen_US
dc.description.abstractSpeech and voice disorders are one of the most significant biomarkers in early diagnosis of Parkinson's disease (PD). The development of an objective, reliable and effective prediction model is crucial for the early detection of PD by experts. The aim of this study is to investigate the effectiveness of musical features of voice recordings on PD and healthy subject discrimination issue. Extracted number of 41 musical features from the voice recordings of 28 PD and 62 healthy controls are used in the context of music information retrieval. These features are employed in the classification models either as a single large set or partitioned into smaller feature groups. Leave-one-subject-out (LOSO), leave-one-out (LOO) and 10-fold cross validation schemes are used while training and testing in support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers by providing statistical measures. The effect of low, normal and high tone voice recordings is also studied separately, and the results show that using low-tone voice recordings may not be useful for discrimination of dysphonic voice. Despite using least number of features of all related schemes which use raw voice recordings, our proposed musical features with LOSO cross validation technique perform better accuracy results than the existing studies.en_US
dc.description.sponsorshipMUEGUETEM,SEIKEI,STUFEI,SOFIA Technical Univ,Lab Informatique Avancee Saint Denis Univ,FERITen_US
dc.identifier.endpage408en_US
dc.identifier.isbn978-1-5386-4695-3
dc.identifier.scopus2-s2.0-85053549826en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage405en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20641
dc.identifier.wosWOS:000454845100091en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2018 41st International Conference On Telecommunications And Signal Processing (Tsp)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDysphoniaen_US
dc.subjectFeature Extractionen_US
dc.subjectMusic Information Retrievalen_US
dc.subjectVoice Analysisen_US
dc.subjectTonal Analysisen_US
dc.titleMusical Feature Based Classification of Parkinson's Disease Using Dysphonic Speechen_US
dc.typeConference Objecten_US

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