Verifying tag annotation and performing genre classification in music data via association analysis

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dc.contributor.authorArjannikov, Tom
dc.contributor.supervisorZhang, John Z.
dc.date.accessioned2015-01-29T19:48:59Z
dc.date.available2015-01-29T19:48:59Z
dc.date.issued2014
dc.degree.levelMastersen_US
dc.degree.levelMasters
dc.description.abstractMusic Information Retrieval aims to automate the access to large-volume music data, including browsing, retrieval, storage, etc. The work presented in this thesis tackles two non-trivial problems in the field. First problem deals with music tags, which provide descriptive and rich information about a music piece, including its genre, artist, emotion, instrument, etc. At present, tag annotation is largely a manual process, which often results in tags that are subjective, ambiguous, and error-prone. We propose a novel approach to verify the quality of tag annotation in a music dataset through association analysis. Second, we employ association analysis to predict music genres based on features extracted directly from music. We build an association-based classifier, which finds inherent associations between music features and genres. We demonstrate the effectiveness of our approaches through a series of simulations and experiments using various benchmark music datasets.en_US
dc.identifier.urihttps://hdl.handle.net/10133/3630
dc.language.isoen_CAen_US
dc.proquest.subject0984en_US
dc.proquest.subject0800en_US
dc.proquest.subject0413en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. 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.subjectdata miningen_US
dc.subjectmachine learningen_US
dc.subjectinformation retrievalen_US
dc.subjectmusic information retrievalen_US
dc.titleVerifying tag annotation and performing genre classification in music data via association analysisen_US
dc.typeThesisen_US
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