Analyzing and enhancing music mood classification : an empirical study

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dc.contributor.authorShahmansouri, Houman
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorZhang, John Z.
dc.contributor.supervisorShan, Gongbing
dc.date.accessioned2017-05-15T17:30:18Z
dc.date.available2017-05-15T17:30:18Z
dc.date.issued2016
dc.degree.levelMastersen_US
dc.description.abstractIn the computer age, managing large data repositories is one of the common challenges, especially for music data. Categorizing, manipulating, and refining music tracks are among the most complex tasks in Music Information Retrieval (MIR). Classification is one of the core functions in MIR, which classifies music data from different perspectives, from genre to instrument to mood. The primary focus of this study is on music mood classification. Mood is a subjective phenomenon in MIR, which involves different considerations, such as psychology, musicology, culture, and social behavior. One of the most significant prerequisitions in music mood classification is answering these questions: what combination of acoustic features helps us to improve the accuracy of classification in this area? What type of classifiers is appropriate in music mood classification? How can we increase the accuracy of music mood classification using several classifiers? To find the answers to these questions, we empirically explored different acoustic features and classification schemes on the mood classification in music data. Also, we found the two approaches to use several classifiers simultaneously to classify music tracks using mood labels automatically. These methods contain two voting procedures; namely, Plurality Voting and Borda Count. These approaches are categorized into ensemble techniques, which combine a group of classifiers to reach better accuracy. The proposed ensemble methods are implemented and verified through empirical experiments. The results of the experiments have shown that these proposed approaches could improve the accuracy of music mood classification.en_US
dc.embargoNoen_US
dc.identifier.urihttps://hdl.handle.net/10133/4846
dc.language.isoen_USen_US
dc.proquest.subject0984en_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.subjectautomatic tag annotationen_US
dc.subjectdata mining toolsen_US
dc.subjectensemble methodsen_US
dc.subjectmachine learning algorithmsen_US
dc.subjectmusic information retrievalen_US
dc.subjectmusic mood classificationen_US
dc.subjectMusic -- Data processingen_US
dc.subjectData miningen_US
dc.subjectMusic -- Databasesen_US
dc.subjectInformation retrieval -- Computer programsen_US
dc.subjectMachine learningen_US
dc.subjectInformation storage and retrieval systems -- Musicen_US
dc.subjectEmotions in music -- Data processingen_US
dc.subjectComputer algorithmsen_US
dc.subjectClassification rule miningen_US
dc.subjectMusic analysis -- Data processingen_US
dc.titleAnalyzing and enhancing music mood classification : an empirical studyen_US
dc.typeThesisen_US
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