Ranking-Based Emotion Recognition for Experimental Music

Emotion recognition is an open problem in Affective Computing the field. Music emotion recognition (MER) has challenges including variability of musical content across genres, the cultural background of listeners, reliability of ground truth data, and the modeling human hearing in computational domains. In this study, we focus on experimental music emotion recognition. First, we present a music corpus that contains 100 experimental music clips and 40 music clips from 8 musical genres. The dataset (the music clips and annotations) is publicly available at: http://metacreation.net/project/emusic/. Then, we present a crowdsourcing method that we use to collect ground truth via ranking the valence and arousal of music clips. Next, we propose a smoothed RankSVM (SRSVM) method. The evaluation has shown that the SRSVM outperforms four other ranking algorithms. Finally, we analyze the distribution of perceived emotion of experimental music against other genres to demonstrate the difference between genres.