Evaluation of content-based acoustic features for musical genre classification
Lai, Yuhui
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https://hdl.handle.net/2142/95426
Description
Title
Evaluation of content-based acoustic features for musical genre classification
Author(s)
Lai, Yuhui
Issue Date
2016-12-09
Director of Research (if dissertation) or Advisor (if thesis)
Hasegawa Johnson, Mark
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Music genre classification
k-nearest neighbor (KNN)
Content-based acoustic feature
Abstract
In this thesis, we evaluate content-based acoustic features for musical genre classification. Effectiveness of various acoustic features are compared using a k-nearest neighbor (KNN) classifier. By utilizing the combinations of acoustic features, an average classification accuracy of $89\%$ for GTZAN database is achieved, which is comparable to prior work. A statistical test, McNemar's test, is applied to support the idea that musical genre is intrinsically related to content-based acoustic features. Especially for some genres, we are able to identify the particular associated acoustic property. In addition, by comparing our KNN results to a psychoacoustic listening experiment, we associate various human perceptual dimensions with low-level acoustic features.
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