Analyzing and improving genre and style classification in music through experiments
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Date
2014
Authors
Ghasemaghai, Zahra
Journal Title
Journal ISSN
Volume Title
Publisher
Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science
Abstract
Music classification is a core task in the field of Music Information Retrieval (MIR).
Classification refers to recognizing patterns in data. Music classification assigns genre,
style, mood and etc. to each piece of music, to facilitate managing music data. It is an
interesting topic in MIR with potential applications.
There has been a considerable deal of attention focused on variety issues of music
classification, such as selection appropriate feature sets, feature selection techniques,
classification algorithm, etc.
In this thesis, a series of empirical experiments are conducted to investigate and evaluate
the genre and style classification in music. To validate our investigations and evaluations,
several methods are proposed to analyze and interpret the results. In addition, we also
design and implement an effective classification approach that obtains higher classification
accuracy.
Description
Keywords
algorithms , music classification , music data , music information retrieval