Music Emotion Recognition with Standard and Melodic Audio Features



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.


Music Emotion Recognition

Related Project

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


Applied Artificial Intelligence, Vol. 29, #4, pp. 313-334, Taylor & Francis 2015

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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.