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Automatic music genre classification using support vector machine and hidden Markov model / Rose -Ann A. Espeleta, Gerryne Carrise N. Tabije.

By: Espeleta, Rose -Ann A [author].
Contributor(s): Tabije ,Gerryne Carrise N [author].
Material type: materialTypeLabelBookManila : Polytechnic University of the Philippines, 2016Description: iii, 51, leaves ; 28 cm.Content type: text Media type: unmediated Carrier type: volumeSubject(s): music genre classification | Vector machine | Markov modelDissertation note: Thesis (Bachelor of Science in Computer Science) -- PUP, 2016. Abstract: The need for an automatic classification of music is increasing now days as the number of recordings continues to grow. Classifying systems capable of performing automatic classifications would be particularly useful to the administrators of the rapidly growing networked music archives ,as their success is linked to ease with which users can search for types of music on their sites. many of these sites rely on manual genre classification which are not that efficient.
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Item type Current location Call number Copy number Status Notes Date due Barcode Item holds
Theses and Dissertation Theses and Dissertation Main Library: Special Collection Section
BSCS Espa 2016 (Browse shelf) c.1 Available Requested by: TU-2491
Total holds: 0

Thesis (Bachelor of Science in Computer Science) -- PUP, 2016.

The need for an automatic classification of music is increasing now days as the number of recordings continues to grow. Classifying systems capable of performing automatic classifications would be particularly useful to the administrators of the rapidly growing networked music archives ,as their success is linked to ease with which users can search for types of music on their sites. many of these sites rely on manual genre classification which are not that efficient.

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