Hidden markov models for music classification.

dc.contributor.authorLiu, Xinru
dc.date.accessioned2020-05-14T13:13:12Z
dc.date.available2020-05-14T13:13:12Z
dc.date.issued2019
dc.descriptionIncludes bibliographical references (leaves 67-70).
dc.descriptioniii, 70 leaves : illustrations
dc.description.tableofcontents1 Introduction -- 2 Music information retrieval -- 2.1 Music information retrieval -- 2.2 Mel-frequency cepstral coefficients -- 2.3 Implementation of MFCCs -- 3 Hidden markov model -- 3.1 Hidden markov model -- 3.1.1 Markov chain -- 3.1.2 Hidden markov models -- 3.2 Forward algorithm -- 3.3 Backward algorithm -- 3.4 Parameter estimation -- 3.4.1 Expectation-maximization algorithm -- 3.4.2 Baum welch algorithm -- 3.5 Similarity metric -- 4 Initialization -- 4.1 Initial parameter estimation -- 4.1.1 Model-based agglomerative hierarchical clustering -- 5 Experiments and results -- 5.1 Composers -- 5.1.1 Bach -- 5.1.2 Beethoven -- 5.1.3 Schubert -- 5.1.4 Chopin -- 5.1.5 Debussy -- 5.1.6 Schumann -- 5.1.7 Schoenberg -- 5.2 Experiments -- 5.3 Discussion -- 5.3.1 Validation -- 5.3.2 Analysis of the result -- 5.3.3 Accuracy -- 5.3.4 Problems and concerns -- 6 Conclusion and Future Work
dc.identifier.otherW Thesis 1565
dc.identifier.urihttps://digitalrepository.wheatoncollege.edu/handle/11040/31248
dc.language.isoen_usen_US
dc.publisherWheaton College (MA)
dc.subjectUndergraduate research.
dc.subjectUndergraduate thesis.
dc.subject.lcshMarkov processes.
dc.subject.lcshMusic--Mathematical models.
dc.subject.lcshClassification--Music.
dc.subject.lcshMusic--Acoustics and physics.
dc.subject.lcshMusic--Information technology.
dc.subject.lcshInformation theory in music.
dc.subject.lcshInformation retrieval.
dc.subject.lcshMusic theory--Mathematics.
dc.titleHidden markov models for music classification.en_US
dc.typeThesisen
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