Evaluation of Musical Features for Emotion Classification

Because music conveys and evokes feelings, a wealth of research has been performed on music emotion recognition. Previous research has shown that musical mood is linked to features based on rhythm, timbre, spectrum and lyrics. For example, sad music correlates with slow tempo, while happy music is generally faster. However, only limited success has been obtained in learning automatic classifiers of emotion in music. In this paper, we collect a ground truth data set of 2904 songs that have been tagged with one of the four words “happy”, “sad”, “angry” and “relaxed”, on the Last.FM web site. An excerpt of the audio is then retrieved from 7Digital.com, and various sets of audio features are extracted using standard algorithms. Two classifiers are trained using support vector machines with the polynomial and radial basis function kernels, and these are tested with 10-fold cross validation. Our results show that spectral features outperform those based on rhythm, dynamics, and, to a lesser extent, harmony. We also find that the polynomial kernel gives better results than the radial basis function, and that the fusion of different feature sets does not always lead to improved classification.