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dc.contributor.authorBond, Zacharyen
dc.date.accessioned2014-03-14T20:50:51Zen
dc.date.available2014-03-14T20:50:51Zen
dc.date.issued2006-11-15en
dc.identifier.otheretd-12282006-020030en
dc.identifier.urihttp://hdl.handle.net/10919/36469en
dc.description.abstractThis thesis describes the ideal properties of an adaptable music classification system based on unsupervised machine learning, and argues that such a system should be based on the fundamental musical properties of timbre, rhythm, melody and harmony. The first two properties and the signal features associated with them are then explored in more depth. In the area of timbre, the relationship between musical style and commonly-extracted signal features within a broad range of piano music is explored, in an effort to identify features which are consistent among all piano music but different for other instruments. The effect of lossy compression on these same timbre features is also investigated. In the area of rhythm, a new tempo tracking tool is provided which produces a series of histograms containing beat and sub-beat information throughout the course of a musical recording. These histograms are then shown to be useful in the analysis of synthesized rhythms and real music. Additionally, a novel method based on the Expectation-Maximization algorithm is used to extract features for classification from the histograms.en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartresults_rhythm.txten
dc.relation.haspartresults_timbre.txten
dc.relation.haspartunsupervised_classification.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectclusteringen
dc.subjectmusicen
dc.subjectrhythmen
dc.subjecttimbreen
dc.subjectclassificationen
dc.titleUnsupervised Classification of Music Signals: Strategies Using Timbre and Rhythmen
dc.typeThesisen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineElectrical and Computer Engineeringen
dc.contributor.committeechairAbbott, A. Lynnen
dc.contributor.committeememberBeex, A. A. Louisen
dc.contributor.committeememberMartin, Thomas L.en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12282006-020030/en
dc.date.sdate2006-12-28en
dc.date.rdate2010-02-06en
dc.date.adate2007-02-06en


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