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Maximum Likelihood Pitch Estimation Using Sinusoidal Modeling

dc.contributor.advisorEspy-Wilson, Carol Yen_US
dc.contributor.authorMahadevan, Vijayen_US
dc.description.abstractThe aim of the work presented in this thesis is to automatically extract the fundamental frequency of a periodic signal from noisy observations, a task commonly referred to as pitch estimation. An algorithm for optimal pitch estimation using a maximum likelihood formulation is presented. The speech waveform is modeled using sinusoidal basis functions that are harmonically tied together to explicitly capture the periodic structure of voiced speech. The problem of pitch estimation is casted as a model selection problem and the Akaike Information Criterion is used to estimate the pitch. The algorithm is compared with several existing pitch detection algorithms (PDAs) on a reference pitch database. The results indicate the superior performance of the algorithm in comparison with most of the PDAs. The application of parametric modeling in single channel speech segregation and the use of mel-frequency cepstral coefficients for sequential grouping are analyzed in the speech separation challenge database.en_US
dc.titleMaximum Likelihood Pitch Estimation Using Sinusoidal Modelingen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentElectrical Engineeringen_US
dc.subject.pqcontrolledElectrical Engineeringen_US
dc.subject.pquncontrolledAkaike Information Criterionen_US
dc.subject.pquncontrolledFundamental Frequencyen_US
dc.subject.pquncontrolledMaximum Likelihooden_US
dc.subject.pquncontrolledSequential Groupingen_US
dc.subject.pquncontrolledSpeech Segregationen_US

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