Multi-label classification of music by emotion

This work studies the task of automatic emotion detection in music. Music may evoke more than one different emotion at the same time. Single-label classification and regression cannot model this multiplicity. Therefore, this work focuses on multi-label classification approaches, where a piece of mus...

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Bibliographic Details
Published in:EURASIP journal on audio, speech, and music processing speech, and music processing, 2011-09, Vol.2011 (1), p.1-9, Article 4
Main Authors: Trohidis, Konstantinos, Tsoumakas, Grigorios, Kalliris, George, Vlahavas, Ioannis
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Classification
Emotions
Engineering
Engineering Acoustics
Mathematical models
Mathematics in Music
Music
Regression
Signal,Image and Speech Processing
Tasks
Work study
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Summary:This work studies the task of automatic emotion detection in music. Music may evoke more than one different emotion at the same time. Single-label classification and regression cannot model this multiplicity. Therefore, this work focuses on multi-label classification approaches, where a piece of music may simultaneously belong to more than one class. Seven algorithms are experimentally compared for this task. Furthermore, the predictive power of several audio features is evaluated using a new multi-label feature selection method. Experiments are conducted on a set of 593 songs with six clusters of emotions based on the Tellegen-Watson-Clark model of affect. Results show that multi-label modeling is successful and provide interesting insights into the predictive quality of the algorithms and features.
ISSN:1687-4722
1687-4714
1687-4722
DOI:10.1186/1687-4722-2011-426793