{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/36469"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/36469","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm","abstract":"This thesis describes the ideal properties of an adaptable music classification system based on unsupervised machine learning, and argues that such a system should be based on the fundamental musical properties of timbre, rhythm, melody and harmony. The first two properties and the signal features associated with them are then explored in more depth. In the area of timbre, the relationship between musical style and commonly-extracted signal features within a broad range of piano music is explored, in an effort to identify features which are consistent among all piano music but different for other instruments. The effect of lossy compression on these same timbre features is also investigated. In the area of rhythm, a new tempo tracking tool is provided which produces a series of histograms containing beat and sub-beat information throughout the course of a musical recording. These histograms are then shown to be useful in the analysis of synthesized rhythms and real music. Additionally, a novel method based on the Expectation-Maximization algorithm is used to extract features for classification from the histograms.","abstract_html":"This thesis describes the ideal properties of an adaptable music classification system based on unsupervised machine learning, and argues that such a system should be based on the fundamental musical properties of timbre, rhythm, melody and harmony. The first two properties and the signal features associated with them are then explored in more depth. In the area of timbre, the relationship between musical style and commonly-extracted signal features within a broad range of piano music is explored, in an effort to identify features which are consistent among all piano music but different for other instruments. The effect of lossy compression on these same timbre features is also investigated. In the area of rhythm, a new tempo tracking tool is provided which produces a series of histograms containing beat and sub-beat information throughout the course of a musical recording. These histograms are then shown to be useful in the analysis of synthesized rhythms and real music. Additionally, a novel method based on the Expectation-Maximization algorithm is used to extract features for classification from the histograms.","abstract_has_math":false,"creators":["Bond, Zachary"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical and Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Abbott, A. Lynn"],"committee_members":["Beex, A. A. Louis","Martin, Thomas L."],"year":2006,"date_issued":"2006-11-15","date_published":"2006-11-15","updated_at":"2026-07-22T22:19:00Z","subjects":["clustering","music","rhythm","timbre","classification"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12282006-020030"],"render_values":[{"text":"etd-12282006-020030","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/36469","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Abbott, A. Lynn"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Beex, A. A. Louis","Martin, Thomas L."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Bond, Zachary"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:50:51Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:50:51Z","2010-02-06"]},{"key":"dc:date.issued","label":"Date","values":["2006-11-15"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["clustering","music","rhythm","timbre","classification"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12282006-020030"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/36469"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis describes the ideal properties of an adaptable music classification system based on unsupervised machine learning, and argues that such a system should be based on the fundamental musical properties of timbre, rhythm, melody and harmony. The first two properties and the signal features associated with them are then explored in more depth. In the area of timbre, the relationship between musical style and commonly-extracted signal features within a broad range of piano music is explored, in an effort to identify features which are consistent among all piano music but different for other instruments. The effect of lossy compression on these same timbre features is also investigated. In the area of rhythm, a new tempo tracking tool is provided which produces a series of histograms containing beat and sub-beat information throughout the course of a musical recording. These histograms are then shown to be useful in the analysis of synthesized rhythms and real music. Additionally, a novel method based on the Expectation-Maximization algorithm is used to extract features for classification from the histograms."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Abbott, A. Lynn"],"dc:contributor.committeemember":["Beex, A. A. Louis","Martin, Thomas L."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Bond, Zachary"],"dc:date.accessioned":["2014-03-14T20:50:51Z"],"dc:date.available":["2014-03-14T20:50:51Z","2010-02-06"],"dc:date.issued":["2006-11-15"],"dc:description.abstract":["This thesis describes the ideal properties of an adaptable music classification system based on unsupervised machine learning, and argues that such a system should be based on the fundamental musical properties of timbre, rhythm, melody and harmony. The first two properties and the signal features associated with them are then explored in more depth. In the area of timbre, the relationship between musical style and commonly-extracted signal features within a broad range of piano music is explored, in an effort to identify features which are consistent among all piano music but different for other instruments. The effect of lossy compression on these same timbre features is also investigated. In the area of rhythm, a new tempo tracking tool is provided which produces a series of histograms containing beat and sub-beat information throughout the course of a musical recording. These histograms are then shown to be useful in the analysis of synthesized rhythms and real music. Additionally, a novel method based on the Expectation-Maximization algorithm is used to extract features for classification from the histograms."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-12282006-020030"],"dc:identifier.uri":["http://hdl.handle.net/10919/36469"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["clustering","music","rhythm","timbre","classification"],"dc:title":["Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:00Z"}