Novel Affective Features For Multiscale Prediction Of Emotion In Music

The majority of computational work on emotion in music concentrates on developing machine learning methodologies to build new, more accurate prediction systems, and usually relies on generic acoustic features. Relatively less effort has been put to the development and analysis of features that are particularly suited for the task. The contribution of this paper is twofold. First, the paper proposes two features that can efficiently capture the emotion-related properties in music. These features are named compressibility and sparse spectral components. These features are designed to capture the overall affective characteristics of music (global features). We demonstrate that they can predict emotional dimensions (arousal and valence) with high accuracy as compared to generic audio features. Secondly, we investigate the relationship between the proposed features and the dynamic variation in the emotion ratings. To this end, we propose a novel Haar transform-based technique to predict dynamic emotion ratings using only global features.