{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95426"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95426","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Evaluation of content-based acoustic features for musical genre classification","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.","abstract_html":"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.","abstract_has_math":true,"creators":["Lai, Yuhui"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Hasegawa Johnson, Mark"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T15:49:36Z","date_published":"2017-03-01T15:49:36Z","updated_at":"2026-07-22T22:26:37Z","subjects":["Music genre classification","k-nearest neighbor (KNN)","Content-based acoustic feature"],"languages":["en"],"rights":["Copyright 2016 Yuhui Lai"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95426","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hasegawa Johnson, Mark"]},{"key":"dc:creator","label":"Author","values":["Lai, Yuhui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T15:49:36Z","2016-12-09","2016-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Music genre classification","k-nearest neighbor (KNN)","Content-based acoustic feature"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Yuhui Lai"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95426"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Yuhui Lai, accepted the attached license on 2016-12-09 at 03:00.","The student, Yuhui Lai, submitted this Thesis for approval on 2016-12-09 at 03:08.","This Thesis was approved for publication on 2016-12-09 at 09:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10498 on 2017-02-28 at 15:04:18","Made available in DSpace on 2017-03-01T15:49:36Z (GMT). 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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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Yuhui Lai, accepted the attached license on 2016-12-09 at 03:00.","The student, Yuhui Lai, submitted this Thesis for approval on 2016-12-09 at 03:08.","This Thesis was approved for publication on 2016-12-09 at 09:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10498 on 2017-02-28 at 15:04:18","Made available in DSpace on 2017-03-01T15:49:36Z (GMT). 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