Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech

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dc.contributor.authorDemircan, Semiye
dc.contributor.authorKahramanli, Humar
dc.date.accessioned2020-03-26T19:52:55Z
dc.date.available2020-03-26T19:52:55Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn the present study, emotion recognition from speech signals was performed by using the fuzzy C-means algorithm. Spectral features obtained from speech signals were used as features. The spectral features used were Mel frequency cepstral coefficients and linear prediction coefficients. Certain statistical features were extracted from the spectral features obtained in the study. After the selection of the extracted features, cluster centers were identified by using type-1 fuzzy C-means (FCM) algorithm and used as input to the classifier. Supervised classifiers such as ANN, NB, kNN, and SVM were used for classification. In the study, all seven emotions of the EmoDB database were used. Of the features obtained, FCM clustering was applied to Mel coefficients and obtained clusters centers were used as input for classification. The results showed that using FCM for preprocessing aim increased the success rate. The comparison of the classification methods showed that the maximum success rate was obtained as 92.86% using the SVM classifier.en_US
dc.description.sponsorshipSelcuk University Scientific Research ProjectsSelcuk University; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThe authors acknowledge the support of this study provided by Selcuk University Scientific Research Projects. The authors also thank TUBITAK for their support of this study.en_US
dc.identifier.doi10.1007/s00521-016-2712-yen_US
dc.identifier.endpage66en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage59en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-016-2712-y
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36340
dc.identifier.volume29en_US
dc.identifier.wosWOS:000427799900005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectEmotion recognitionen_US
dc.subjectMFCCen_US
dc.subjectLPCen_US
dc.subjectFuzzy C-meansen_US
dc.titleApplication of fuzzy C-means clustering algorithm to spectral features for emotion classification from speechen_US
dc.typeArticleen_US

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