Automatic music genre classi cation is a high-level task in the eld of Music Information
Retrieval (MIR). It refers to the process of automatically assigning genre labels to music
for various tasks, including, but not limited to categorization, organization and browsing.
This is a topic which has seen an increase in interest recently as one of the cornerstones of
MIR. However, due to the subjective and ambiguous nature of music, traditional single-label
classi cation is inadequate.
In this thesis, we study multi-label music genre classi cation from perceptual and computational
perspectives. First, we design a set of perceptual experiments to investigate
the genre-labelling behavior of individuals. The results from these experiments lead us to
speculate that multi-label classi cation is more appropriate for classifying music genres.
Second, we design a set of computational experiments to evaluate multi-label classi cation
algorithms on music. These experiments not only support our speculation but also reveal
which algorithms are more suitable for music genre classi cation. Finally, we propose and
examine a group of ensemble approaches for combining multi-label classi cation algorithms
to further improve classi cation performance.
ii / viii, 87 leaves ; 29 cm
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:ALU.w.uleth.ca/dspace#10133/2602 |
| Date | January 2010 |
| Creators | Sanden, Christopher, University of Lethbridge. Faculty of Arts and Science |
| Contributors | Zhang, John |
| Publisher | Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, Arts and Science, Department of Mathematics and Computer Science |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Relation | Thesis (University of Lethbridge. Faculty of Arts and Science) |
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