A software system that automatically classifies MIDI files into hierarchically organized taxonomies of musical genres is presented. This extensible software includes an easy to use and flexible GUI. An extensive library of high-level musical features is compiled, including many original features. A novel hybrid classification system is used that makes use of hierarchical, flat and round robin classification. Both k-nearest neighbour and neural network-based classifiers are used, and feature selection and weighting are performed using genetic algorithms. A thorough review of previous research in automatic genre classification is presented, along with an overview of automatic feature selection and classification techniques. Also included is a discussion of the theoretical issues relating to musical genre, including but not limited to what mechanisms humans use to classify music by genre and how realistic genre taxonomies can be constructed.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.81503 |
| Date | January 2004 |
| Creators | McKay, Cory |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Master of Arts (Faculty of Music.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 002182934, proquestno: AAIMR06520, Theses scanned by UMI/ProQuest. |
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