We propose an approach to the dimensional music emotion recognition (MER) problem, combining both standard and melodic audio features. The dataset proposed by Yang is used, which consists of 189 audio clips. From the audio data, 458 standard features and 98 melodic features were extracted. We experimented with several supervised learning and feature selection strategies to evaluate the proposed approach. Employing only standard audio features, the best attained performance was 63.2% and 35.2% for arousal and valence prediction, respectively (R2 statistics). Combining standard audio with melodic features, results improved to 67.4 and 40.6%, for arousal and valence, respectively. To the best of our knowledge, these are the best results attained so far with this dataset.
Subject
music emotion recognition
Related Project
MOODetector: A System for Mood-based Classification and Retrieval of Audio Music
Conference
10th International Symposium on Computer Music Multidisciplinary Research – CMMR’2013, Marseille, France., October 2013
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Cited by
Year 2016 : 4 citations
Wang, Ju-Chiang, Yi-Hsuan Yang, and Hsin-Min Wang. "Affective music information retrieval." Emotions and Personality in Personalized Services. Springer International Publishing, 2016. 227-261.
Witteveen, Jeroen. "Predicting Relevance of Emotion Tags". MSc thesis. Faculty of Humanities, Utrecht University, 2016.
Weihs, Claus, et al., "Music Data Analysis: Foundations and Applications." Taylor & Francis. (2016). ISBN: 978-1-4987-1956-8 / 978-1-4987-1957-5
Van Balen, J. M. H. "Audio description and corpus analysis of popular music". PhD Thesis. Utrecht University, 2016.
Year 2015 : 2 citations
Vatolkin, Igor, Günter Rudolph, and Claus Weihs. "Interpretability of Music Classification as a Criterion for Evolutionary Multi-objective Feature Selection." International Conference on Evolutionary and Biologically Inspired Music and Art. Springer International Publishing, 2015.
Dufour, I. (2015). Improving Music Mood Annotation Using Polygonal Circular Regression. MSc Thesis. Department of Computer Science, University of Victoria, Victoria, BC, Canada.
