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Title:Evaluation of content-based acoustic features for musical genre classification
Author(s):Lai, Yuhui
Advisor(s):Hasegawa Johnson, Mark
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(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.
Issue Date:2016-12-09
Type:Thesis
URI:http://hdl.handle.net/2142/95426
Rights Information:Copyright 2016 Yuhui Lai
Date Available in IDEALS:2017-03-01
Date Deposited:2016-12


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