MOODetector: A Prototype Software Tool for Mood-based Playlist Generation

Abstract

We propose a prototype software tool for the automatic generation of mood-based playlists. The tool works as typical music player, extended with mechanisms for automatic estimation of arousal and valence values in the Thayer plane (TP). Playlists are generated based on one seed song or a desired mood trajectory path drawn by the user, according to the distance to the seed(s) in the TP. Besides playlist generation, a mood tracking visualization tool is also implemented, where individual songs are segmented and classified according to the quadrants in the TP. Additionally, the methodology for music emotion recognition, tackled in this paper as a regression and classification problem, is described, along with the process for feature extraction and selection. Experimental results for mood regression are slightly higher than the state of the art, indicating the viability of the followed strategy (in terms of R2 statistics, arousal and valence estimation accuracy reached 63% and 35.6%, respectively).