Emotion-based music recommendation using audio features and user playlist

Proceedings - 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012

Volumn , Issue , 2012, Pages 796-801

  Deng, James J ^a   Leung, Clement ^a  

^a HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

conditional random fields; graph embedding; Music emotion; music recommendation; rank

Indexed keywords

CONDITIONAL RANDOM FIELD; GRAPH EMBEDDINGS; MUSIC EMOTIONS; MUSIC RECOMMENDATION; RANK;

ALGORITHMS; DATA MINING; INFORMATION SCIENCE; OPTIMIZATION;

RANDOM PROCESSES;

EID: 84881021574     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17: 734-749, 2005.
2. E. Bigand, S. Vieillard, F. Madurell, J. Marozeau, and A. Dacquet. Multidimensional scaling of emotional responses to music: The effect of musical expertise and of the duration of the excerpts. Cognition & Emotion, 19(8): 1113-1139, 2005.
3. P. Gebhard. Alma: a layered model of affect. In Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages, pages 29-36, 2005.
4. X. Hu and J. S. Downie. Exploring mood metadata: Relationships with genre, artist and usage metadata. In International Symposium/Conference on Music Information Retrieval.
5. S. Jun, S. Rho, and E. Hwang. Music retrieval and recommendation scheme based on varying mood sequences. Int. J. Semantic Web Inf. Syst., 6(2): 1-16, 2010.
6. Y. E. Kim, E. M. Schmidt, R. Migneco, B. G. Morton, P. Richardson, J. Scott, J. A. Speck, and D. Turnbull. Music emotion recognition: a state of the art review. Information Retrieval, (Ismir): 255-266, 2010.
7. J. Lafferty, A. McCallum, and F. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Volume pages, pages 282-289. Citeseer, 2001.
8. C. C. Lu and V. S. Tseng. A novel method for personalized music recommendation. Expert Systems with Applications, 36(6): 10035-10044, 2009.
9. L. L. L. Lu, D. Liu, and H.-J. Z. H.-J. Zhang. Automatic mood detection and tracking of music audio signals, 2006.
10. B. McFee, L. Barrington, and G. R. G. Lanckriet. Learning content similarity for music recommendation. IEEE Transactions on Audio, Speech & Language Processing, 20(8): 2207-2218, 2012.
11. A. Ortony, G. L. Clore, and A. Collins. The Cognitive Structure of Emotions, Volume 18. Cambridge University Press, 1988.
12. R. W. Picard. Affective Computing, Volume 136. MIT Press, 1997.
13. U. Schimmack and R. Reisenzein. Experiencing activation: energetic arousal and tense arousal are not mixtures of valence and activation. Emotion Washington Dc, 2(4): 412-417, 2002.
14. E. Schmidt and Y. Kim. Modeling musical emotion dynamics with conditional random fields. In ISMIR'11, pages 777-782, 2011.
15. E. M. Schmidt, D. Turnbull, and Y. E. Kim. Feature selection for content-based, time-varying musical emotion regression categories and subject descriptors. Spectrum, pages 267-273, 2010.
16. B. Shao, M. Ogihara, D. Wang, and T. Li. Music recommendation based on acoustic features and user access patterns. IEEE Transactions on Audio, Speech & Language Processing, 17(8): 1602-1611, 2009.
17. J. Shen, J. Shepherd, and A. H. H. Ngu. Towards effective content-based music retrieval with multiple acoustic feature combination, 2006.
18. B. R. Steunebrink, M. Dastani, and J.-J. C. Meyer. A formal model of emotions: Integrating qualitative and quantitative aspects. Language, (September): 1-5, 2008.
19. R. Thayer. The biopsychology of mood and arousal. Oxford University Press, USA, 1989.
20. S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin. Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1): 40-51, 2007.
21. Y. Yang and H. Chen. Ranking-based emotion recognition for music organization and retrieval. IEEE Transactions On Audio Speech And Language Processing, 19(99): 762-774, 2010.
22. Y.-h. Yang, Y.-f. Su, Y.-c. Lin, and H. H. Chen. Music emotion recognition: The role of individuality. Work, pages 13-21, 2007.
23. Y. Zhang, J. Tang, J. Sun, Y. Chen, and J. Rao. Moodcast: Emotion prediction via dynamic continuous factor graph model. 2010 IEEE International Conference on Data Mining, pages 1193-1198, 2010.