Ranking-Based Emotion Recognition for Experimental Music
Jianyu Fan, Kıvanç Tatar, Miles Thorogood, Philippe Pasquier
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, relia- bility of ground truth data, and the modeling human hear- ing 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 out- performs four other ranking algorithms. Finally, we ana- lyze the distribution of perceived emotion of experi- mental music against other genres to demonstrate the dif- ference between genres.
Type : Conference Paper
Publication : International Society for Music Information Retrieval Conference, ISMIR 2017.