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Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Feature ExtractionHas files: Yes

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    PublicationOpen Access
    Use of line spectral frequencies for emotion recognition from speech
    (2010) Bozkurt, Elif; Erzin, Engin; Eroğlu Erdem, Çiğdem; Erdem, Tanju; Bozkurt, Elif, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, Koç Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Özyeğin Üniversitesi, Istanbul, Turkey
    We propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features such as mel-scaled cepstral coefficients have already been successfully used for the parameterization of speech signals for emotion recognition. The LSF features also offer a spectral representation for speech, moreover they carry intrinsic information on the formant structure as well, which are related to the emotional state of the speaker [4]. We use the Gaussian mixture model (GMM) classifier architecture, that captures the static color of the spectral features. Experimental studies performed over the Berlin Emotional Speech Database and the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF features bring a consistent improvement over the MFCC based emotion classification rates. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.
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    PublicationOpen Access
    Simultaneous feature selection and ant colony clustering
    (2011) Akarsu, Emre; Karahoca, Adem; Akarsu, Emre, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Karahoca, Adem, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey
    Clustering is a widely studied problem in data mining. Ai techniques, evolutionary techniques and optimization techniques are applied to this field. In this study, a novel hybrid modeling approach proposed for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer data set. To remove irrelevant or redundant features from data set for clustering Sequential Backward Search feature selection technique is applied. Feature selection and clustering algorithms are incorporated as a Wrapper. The results show that, the accuracy of the FS-ACO clustering approach is better than the filter approaches. © 2010 Published by Elsevier Ltd. © 2011 Elsevier B.V., All rights reserved.