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



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).


music emotion recognition, music information retrieval


Music Information Retrieval

Related Project

MOODetector: A System for Mood-based Classification and Retrieval of Audio Music


Simpósio de Informática - INForum 2011, September 2011

Cited by

Year 2016 : 1 citations

 Dias, Ricardo, Daniel Gonçalves, and Manuel J. Fonseca. "From manual to assisted playlist creation: a survey." Multimedia Tools and Applications (2016): 1-29.

Year 2015 : 1 citations

 Gajjar, Kunjal, and Siddhi Shah. "Mood based Playlist Generation for Hindi Popular Music: A Proposed Model." International Journal of Computer Applications (0975–8887) Volume (2015).

Year 2014 : 1 citations

 Ramnani, Sweety, and Ravi Prakash Gorthi. "A Model to Incorporate Emotional Sensitivity into Human Computer Interactions." Proceedings of the 2014 workshop on Emotion Representation and Modelling in Human-Computer-Interaction-Systems. ACM, 2014.