Cost-sensitive multi-label learning for audio tag annotation and retrieval

IEEE Transactions on Multimedia

Volumn 13, Issue 3, 2011, Pages 518-529

  Lo, Hung Yi ^a,b   Wang, Ju Chiang ^a,b   Wang, Hsin Min ^b   Lin, Shou De ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

Author keywords

Audio tag annotation; audio tag retrieval; cost sensitive learning; multi label; tag count

Indexed keywords

AUDIO TAG ANNOTATION; AUDIO TAG RETRIEVAL; COST-SENSITIVE LEARNING; MULTI-LABEL; TAG COUNT;

COSTS; USER INTERFACES;

AUDIO ACOUSTICS;

EID: 79957460742     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2011.2129498     Document Type: Conference Paper

References (27)

1. P. Lamere, "Social tagging and music information retrieval," J. New Music Res., vol. 37, no. 2, pp. 101-114, 2008.
2. D. Eck, P. Lamere, T. Bertin-Mahieux, and S. Green, "Automatic generation of social tags for music recommendation," in Proc. Advances in Neural Information Processing Systems, 2007.
3. M. Hoffman, D. Blei, and P. Cook, "Easy as CBA: A simple probabilistic model for tagging music," in Proc. Int. Soc. Music Information Retrieval Conf., 2009.
4. H.-Y. Lo, J.-C. Wang, and H.-M.Wang, "Homogeneous segmentation and classifier ensemble for audio tag annotation and retrieval," in Proc. IEEE Int. Conf. Multimedia and Expo., 2010.
5. S. Ness, A. Theocharis, L. G. Martins, and G. Tzanetakis, "Improving automatic music tag annotation using stacked generalization of probabilistic SVM outputs," in Proc. ACM Int. Conf. Multimedia, 2009.
6. D. Tingle, Y. Kim, and D. Turnbull, "Exploring automatic music annotation with "acoustically-objective" tags," in Proc. ACM Int. Conf. Multimedia Information Retrieval, 2010.
7. D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet, "Semantic annotation and retrieval of music and sound effects," IEEE Trans. Audio, Speech, Lang. Process., vol. 16, pp. 467-476, 2008.
8. J. Foote and M. Cooper, "Media segmentation using self-similarity decomposition," in Proc. SPIE Storage and Retrieval for Multimedia Databases, 2003.
9. M. I. Mandel and D. P. W. Ellis, "A web-based game for collecting music metadata," J. New Music Res., vol. 37, no. 2, pp. 151-165, 2008.
10. X. Li, C. G. M. Snoek, and M.Worring, "Learning social tag relevance by neighbor voting," IEEE Trans. Multimedia, vol. 11, pp. 1310-1322, 2009.
11. J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and B. Kegl, "Aggregate features and ADABOOST for music classification," Mach. Learn., vol. 65, no. 2-3, pp. 473-484, 2006. (Pubitemid 44836054)
12. M. I. Mandel and D. P. W. Ellis, "Multiple-instance learning for music information retrieval," in Proc. Int. Conf. Music Information Retrieval, 2007.
13. G. Tsoumakas, I. Katakis, and I. Vlahavas, "Mining multi-label data," in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Eds. New York: Springer, 2010.
14. H.-T. Lin, "Cost-sensitive classification: Status and beyond," in Proc. Workshop Machine Learning Research in Taiwan: Challenges and Directions, 2010.
15. R. Miotto, L. Barrington, and G. Lanckriet, "Improving auto-tagging by modeling semantic co-occurrences," in Proc. Int. Soc. Music Information Retrieval Conf., 2010.
16. L. Cao, J. Yu, J. Luo, and T. S. Huang, "Enhancing semantic and geographic annotation of web images via logistic canonical correlation regression," in Proc. ACM Int. Conf. Multimedia, 2009.
17. D. H. Wolpert, "Stacked generalization," Neural Netw., vol. 5, no. 2, pp. 241-259, 1992.
18. G. Tsoumakas and I. Vlahavas, "Random K-labelsets: An ensemble method for multilabel classification," in Proc. Eur. Conf. Machine Learning, 2007.
19. O. Lartillot and P. Toiviainen, "A Matlab toolbox for musical feature extraction from audio," in Proc. Int. Conf. Digital Audio Effects, 2007.
20. C. Cortes and V. Vapnik, "Support vector networks," Mach. Learn., vol. 20, pp. 273-279, 1995.
21. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," J. Comput. Syst. Sci., vol. 55, no. 1, pp. 119-139, 1997. (Pubitemid 127433398)
22. Y. Sun, M. S. Kamel, A. K. C. Wong, and Y. Wang, "Cost-sensitive boosting for classification of imbalanced data," Pattern Recognit., vol. 40, no. 12, pp. 3358-3378, 2007. (Pubitemid 47223287)
23. J. Platt, "Probabilistic outputs for support vector machines and comparison to regularized likelihood methods," in Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Eds. Cambridge, MA: MIT Press, 1998.
24. H.-T. Lin, C.-J. Lin, and R. C. Weng, "A note on Platt's probabilistic outputs for support vector machines," Mach. Learn., vol. 68, no. 3, pp. 267-276, 2007. (Pubitemid 47312490)
25. B. Zadrozny, J. Langford, and N. Abe, "Cost-sensitive learning by cost-proportionate example weighting," in Proc. IEEE Int. Conf. Data Mining, 2003.
26. H.-T. Lin, "A Simple Cost-Sensitive Multiclass Classification Algorithm using One-Versus-One Comparisons," Tech. Rep., National Taiwan Univ., 2010.
27. X. Hu, J. S. Downie, and A. Ehmann, "Lyric text mining in music mood classification," in Proc. Int. Soc. Music Information Retrieval Conf., 2009.