Multi-label classification of music by emotion

K. Trohidis, G. Tsoumakas, G. Kalliris, I. Vlahavas, “Multi-label classification of music by emotion”, EURASIP Journal on Audio, Speech, and Music Processing, 2011:4, 2011.

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.