Evaluation of content-based acoustic features for musical genre classification

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Lai, Yuhui

Contributors dc:contributor

• Hasegawa Johnson, Mark

• Music genre classification
• k-nearest neighbor (KNN)
• Content-based acoustic feature

Statement dc:rights

• Copyright 2016 Yuhui Lai

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/95426

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/95426