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dc.contributor.supervisor Zhang, John Z.
dc.contributor.supervisor Shan, Gongbing
dc.contributor.author Shahmansouri, Houman
dc.contributor.author University of Lethbridge. Faculty of Arts and Science
dc.date.accessioned 2017-05-15T17:30:18Z
dc.date.available 2017-05-15T17:30:18Z
dc.date.issued 2016
dc.identifier.uri https://hdl.handle.net/10133/4846
dc.description.abstract In 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.language.iso en_US en_US
dc.publisher Lethbridge, Alta : University of Lethbridge, Dept. of Mathematics and Computer Science en_US
dc.relation.ispartofseries Thesis (University of Lethbridge. Faculty of Arts and Science) en_US
dc.subject automatic tag annotation en_US
dc.subject data mining tools en_US
dc.subject ensemble methods en_US
dc.subject machine learning algorithms en_US
dc.subject music information retrieval en_US
dc.subject music mood classification en_US
dc.subject Music -- Data processing en_US
dc.subject Data mining en_US
dc.subject Music -- Databases en_US
dc.subject Information retrieval -- Computer programs en_US
dc.subject Machine learning en_US
dc.subject Information storage and retrieval systems -- Music en_US
dc.subject Emotions in music -- Data processing en_US
dc.subject Computer algorithms en_US
dc.subject Classification rule mining en_US
dc.subject Music analysis -- Data processing en_US
dc.title Analyzing and enhancing music mood classification : an empirical study en_US
dc.type Thesis en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Mathematics and Computer Science en_US
dc.degree.level Masters en_US
dc.proquest.subject 0984 en_US
dc.proquestyes Yes en_US
dc.embargo No en_US


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