Musical Texture and Expressivity Features for Music Emotion Recognition

Abstract

We present a set of novel emotionally-relevant audio features to help improving the classification of emotions in audio music. First, a review of the state-of-the-art regarding emotion and music was conducted, to understand how the various music concepts may influence human emotions. Next, well known audio frameworks were analyzed, assessing how their extractors relate with the studied musical concepts. The intersection of this data showed an unbalanced representation of the eight musical concepts. Namely, most extractors are low-level and related with tone color, while musical form, musical texture and expressive techniques are lacking. Based on this, we developed a set of new algorithms to capture information related with musical texture and expressive techniques, the two most lacking concepts. To validate our work, a public dataset containing 900 30-second clips, annotated in terms of Russell's emotion quadrants was created. The inclusion of our features improved the F1-score obtained using the best 100 features by 8.6% (to 76.0%), using support vector machines and 20 repetitions of 10-fold cross-validation.