Characterizing and classifying music genres and subgenres via association analysis

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dc.contributor.authorLefaivre, Adam T.
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorZhang, John Z.
dc.date.accessioned2019-06-27T16:31:40Z
dc.date.available2019-06-27T16:31:40Z
dc.date.issued2019
dc.degree.levelMastersen_US
dc.description.abstractIn this thesis, we investigate the problem of automatic music genre classification in the field of Music Information Retrieval (MIR). MIR seeks to apply convenient automated solutions to many music-related tasks that are too tedious to perform by hand. These tasks often deal with vast quantities of music data. An effective automatic music genre classification approach may be useful for other tasks in MIR as well. Association analysis is a technique used to explore the inherent relationships among data objects in a problem domain. We present two novel approaches which capture genre characteristics through the use of association analysis on large music datasets. The first approach extracts the characteristic features of genres and uses these features to perform classification. The second approach attempts to improve on the first one by utilizing a pairwise dichotomy-like strategy. We then consider applying the second approach to the problem of automatic subgenre classification.en_US
dc.embargoNoen_US
dc.identifier.urihttps://hdl.handle.net/10133/5438
dc.language.isoen_USen_US
dc.proquest.subject0984en_US
dc.proquest.subject0413en_US
dc.proquest.subject0723en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : Universtiy of Lethbridge, Department of Mathematics and Computer Scienceen_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectMusic Information Retrievalen_US
dc.subjectGenre Classificationen_US
dc.subjectAssociation Analysisen_US
dc.subjectData Miningen_US
dc.subjectMachine Learningen_US
dc.subjectMusic -- Data processingen_US
dc.subjectMusical analysis -- Data processingen_US
dc.subjectPopular music genres -- Data processingen_US
dc.subjectAssociation rule miningen_US
dc.subjectInformation retrievalen_US
dc.titleCharacterizing and classifying music genres and subgenres via association analysisen_US
dc.typeThesisen_US
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