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dc.contributor.advisorÇetin, A. Enis
dc.contributor.authorBen Fatma, Khaled
dc.date.accessioned2016-01-08T20:17:21Z
dc.date.available2016-01-08T20:17:21Z
dc.date.issued2000
dc.identifier.urihttp://hdl.handle.net/11693/18220
dc.descriptionAnkara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent Univ., 2000.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2000.en_US
dc.descriptionIncludes bibliographical references leaves 49-52.en_US
dc.description.abstractTwo new design algorithms for estimating the parameters of Gaussian Mixture Models (GMh-l) are developed. These algorithms are based on fitting a GMM on the histogram of the data. The first method uses Least Squares Error (LSE) estimation with Gaus,s-Newton optimization technique to provide more accurate GMM parameter estimates than the commonl}' used ExpectationMaximization (EM) algorithm based estimates. The second method employs the matching pursuit algorithm which is based on finding the Gaussian functions that best match the individual components of a GMM from an overcomplete set. This algorithm provides a fast method for obtaining GMM parameter estimates. The proposed methods can be used to model the distribution of a large set of arbitrary random variables. Application of GMMs in human skin color density modeling and speaker recognition is considered. For speaker recognition, a new set of speech fiiature jmrameters is developed. The suggested set is more appropriate for speaker recognition applications than the widely used Mel-scale based one.en_US
dc.description.statementofresponsibilityBen Fatma, Khaleden_US
dc.format.extentxiii, 52 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGaussian Mixture Modelsen_US
dc.subjectParameter Estimationen_US
dc.subjectExpectation-Maximization Algorithmen_US
dc.subjectGauss-Newton Algorithmen_US
dc.subjectMatching Pursuit Algorithmen_US
dc.subjectLeast Sciuares Erroren_US
dc.subjectSpeaker Recognitionen_US
dc.subject.lccQA274.4 .B46 2000en_US
dc.subject.lcshGaussian processes.en_US
dc.subject.lcshGaussian distribution.en_US
dc.subject.lcshEstimation theory.en_US
dc.titleGaussian mixture models design and applicationsen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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