Emotion-based music recommendation by affinity discovery from film music |
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| Authors: | Man-Kwan Shan Fang-Fei Kuo Meng-Fen Chiang Suh-Yin Lee |
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| Affiliation: | 1. School of Computer Science, China University of Geosciences, Wuhan, China;2. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China;3. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China;4. School of Computer Science and Engineering, Kyungpook National University, South Korea;1. College of Computer Science and Information Technology, University of Basrah, Basrah, Iraq;2. Department of Computer Science, University of Technology, Baghdad, Iraq;1. Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan, ROC;2. Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan, ROC |
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| Abstract: | With the growth of digital music, the development of music recommendation is helpful for users to pick desirable music pieces from a huge repository of music. The existing music recommendation approaches are based on a user’s preference on music. However, sometimes, it might better meet users’ requirement to recommend music pieces according to emotions. In this paper, we propose a novel framework for emotion-based music recommendation. The core of the recommendation framework is the construction of the music emotion model by affinity discovery from film music, which plays an important role in conveying emotions in film. We investigate the music feature extraction and propose the Music Affinity Graph and Music Affinity Graph-Plus algorithms for the construction of music emotion model. Experimental result shows the proposed emotion-based music recommendation achieves 85% accuracy in average. |
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| Keywords: | Music recommendation Emotion detection Affinity discovery |
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