Application of ABM to Spectral Features for Emotion Recognition

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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.abstractER (Emotion Recognition) from speech signals has been among the attractive subjects lately. As known feature extraction and feature selection are most important process steps in ER from speech signals. The aim of present study is to select the most relevant spectral feature subset. The proposed method is based on feature selection with optimization algorithm among the features obtained from speech signals. Firstly, MFCC (Mel-Frequency Cepstrum Coefficients) were extracted from the EmoDB. Several statistical values as maximum, minimum, mean, standard deviation, skewness, kurtosis and median were obtained from MFCC. The next process of study was feature selection which was performed in two stages: In the first stage ABM (Agent-Based Modelling) that is hardly applied to this area was applied to actual features. In the second stageOpt-aiNET optimization algorithm was applied in order to choose the agent group giving the best classification success. The last process of the study is classification. ANN (Artificial Neural Network) and 10 cross-validations were used for classification and evaluation. A narrow comprehension with three emotions was performed in the application. As a result, it was seen that the classification accuracy was rising after applying proposed method. The method was shown promising performance with spectral features.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.doi10.22581/muet1982.1804.01en_US
dc.identifier.endpage462en_US
dc.identifier.issn0254-7821en_US
dc.identifier.issn2413-7219en_US
dc.identifier.issue4en_US
dc.identifier.pmid#YOKen_US
dc.identifier.startpage453en_US
dc.identifier.urihttps://dx.doi.org/10.22581/muet1982.1804.01
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36339
dc.identifier.volume37en_US
dc.identifier.wosWOS:000445720300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherMEHRAN UNIV ENGINEERING & TECHNOLOGYen_US
dc.relation.ispartofMEHRAN UNIVERSITY RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAgent-Based Modellingen_US
dc.subjectEmotion recognitionen_US
dc.subjectFeature Extractionen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectOptimizationen_US
dc.titleApplication of ABM to Spectral Features for Emotion Recognitionen_US
dc.typeArticleen_US

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