The computer classification of musical audio can form the basis for systems that allow new ways of interacting with digital music collections. Existing music classification systems suffer, however, from inaccuracy as well as poor scalability. Feature selection is a machine-learning tool that can potentially improve both accuracy and scalability of classification. Unfortunately, there is no consensus on which feature selection algorithms are most appropriate or on how to evaluate the effectiveness of feature selection. Based on relevant literature in music information retrieval (MIR) and machine learning and on empirical testing, the thesis specifies an appropriate evaluation method for feature selection, employs this method to compare existing feature selection algorithms, and evaluates an appropriate feature selection algorithm on the problem of musical genre classification. The outcomes include an increased understanding of the potential for feature selection to benefit MIR and a new technique for optimizing one type of classification-based system.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.99372 |
| Date | January 2006 |
| Creators | Fiebrink, Rebecca. |
| 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 (Schulich School of Music.) |
| Rights | © Rebecca Fiebrink, 2006 |
| Relation | alephsysno: 002572635, proquestno: AAIMR28557, Theses scanned by UMI/ProQuest. |
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