Yi-Hsuan Yang
Yu-Ching Lin
ABSTRACT
In recent years, there has been a dramatic proliferation of research on information retrieval based on highly subjective concepts such as emotion, preference and aesthetic. Such retrieval methods are fascinating but challenging since it is difficult to built a general retrieval model that performs equally well to everyone. In this paper, we propose two novel methods, bag-of-users model and residual modeling, to accommodate the individual differences for emotion-based music retrieval. The proposed methods are intuitive and generally applicable to other information retrieval tasks that involve subjective perception. Evaluation result shows the effectiveness of the proposed methods.
- Y.-H. Yang et al, "A regression approach to music emotion recognition," IEEE Transactions on Audio, Speech and Language Processing, vol. 16, no. 2, pp. 448--457, 2008. Google Scholar
Digital Library
- A. S. Harpale and Y.-M. Yang, "Personalized active learning for collaborative filtering," ACM SIGIR, pp.259--266, 2008. Google ScholarDigital Library
- C. Dorai et al, "Computational media aesthetics: finding meaning beautiful," IEEE Multimedia, vol. 8, no. 4, 2001. Google ScholarDigital Library
- R. E. Thayer, The Biopsychology of Mood and Arousal, New York, Oxford University Press, 1989.Google Scholar
- M. A. Casey et al, "Content--based music information retrieval: Current directions and future challenges," Proceedings of the IEEE, vol. 96, no. 4, pp. 668--696, 2008.Google ScholarCross Ref
- C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," 2001.Google Scholar
- A. Sen and M. Srivastava, Regression Analysis: Theory, Methods, and Applications, New York, Springer, 1990.Google Scholar
Index Terms
- Personalized music emotion recognition
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