Analyzing and enhancing music mood classification : an empirical study
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Date
2016
Authors
Shahmansouri, Houman
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
Abstract
In the computer age, managing large data repositories is one of the common challenges,
especially for music data. Categorizing, manipulating, and refining music tracks are among
the most complex tasks in Music Information Retrieval (MIR). Classification is one of the
core functions in MIR, which classifies music data from different perspectives, from genre
to instrument to mood. The primary focus of this study is on music mood classification.
Mood is a subjective phenomenon in MIR, which involves different considerations, such
as psychology, musicology, culture, and social behavior. One of the most significant prerequisitions
in music mood classification is answering these questions: what combination
of acoustic features helps us to improve the accuracy of classification in this area? What
type of classifiers is appropriate in music mood classification? How can we increase the
accuracy of music mood classification using several classifiers?
To find the answers to these questions, we empirically explored different acoustic features
and classification schemes on the mood classification in music data. Also, we found the two
approaches to use several classifiers simultaneously to classify music tracks using mood labels
automatically. These methods contain two voting procedures; namely, Plurality Voting
and Borda Count. These approaches are categorized into ensemble techniques, which combine
a group of classifiers to reach better accuracy. The proposed ensemble methods are
implemented and verified through empirical experiments. The results of the experiments
have shown that these proposed approaches could improve the accuracy of music mood
classification.
Description
Keywords
automatic tag annotation , data mining tools , ensemble methods , machine learning algorithms , music information retrieval , music mood classification , Music -- Data processing , Data mining , Music -- Databases , Information retrieval -- Computer programs , Machine learning , Information storage and retrieval systems -- Music , Emotions in music -- Data processing , Computer algorithms , Classification rule mining , Music analysis -- Data processing