An empirical evaluation of computational and perceptual multi-label genre classification on music / Christopher Sanden
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Date
2010
Authors
Sanden, Christopher
University of Lethbridge. Faculty of Arts and Science
Journal Title
Journal ISSN
Volume Title
Publisher
Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010
Abstract
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
Description
viii, 87 leaves ; 29 cm
Keywords
Information storage and retrieval systems -- Music , Music -- Data processing , Dissertations, Academic