We propose a novel approach to music emotion recognition by combining standard and melodic features extracted directly from audio. To this end, a new audio dataset organized similarly to the one used in MIREX mood task comparison was created. From the data, 253 standard and 98 melodic features are extracted and used with several supervised learning techniques. Results show that, generally, melodic features perform better than standard audio. The best result, 64% f-measure, with only 11 features (9 melodic and 2 standard), was obtained with ReliefF feature selection and Support Vector Machines.
Subject
Music Emotion Recognition
Related Project
MOODetector: A System for Mood-based Classification and Retrieval of Audio Music
Journal
Applied Artificial Intelligence, Vol. 29, #4, pp. 313-334, Taylor & Francis 2015
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Cited by
Year 2016 : 1 citations
Saim Shin, Sei-Jin Jang, Donghyun Lee, Unsang Park and Ji-Hwan Kim, "Brainwave-based Mood Classification Using Regularized Comm," KSII Transactions on Internet and Information Systems, vol. 10, no. 2, pp. 807-824, 2016. DOI: 10.3837/tiis.2016.02.020
Year 2015 : 1 citations
Dufour, I. (2015). Improving Music Mood Annotation Using Polygonal Circular Regression. MSc Thesis. Department of Computer Science, University of Victoria, Victoria, BC, Canada.
