Preference music ratings prediction using tokenization and minimum classification error training

IEEE Transactions on Audio, Speech and Language Processing

Volumn 19, Issue 8, 2011, Pages 2294-2303

  Reed, Jeremy ^a   Lee, Chin Hui ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Acoustic segment modeling (ASM); content based recommendation; discriminative training; minimum classification error (MCE); music information retrieval (MIR); preference rating

Indexed keywords

CONTENT-BASED RECOMMENDATION; DISCRIMINATIVE-TRAINING; MINIMUM CLASSIFICATION ERROR; MUSIC INFORMATION RETRIEVAL; PREFERENCE RATING; SEGMENT MODELING;

ALGORITHMS; INFORMATION RETRIEVAL; PROBABILITY DENSITY FUNCTION;

COMPUTER MUSIC;

EID: 80052068194     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2011.2129509     Document Type: Article

References (45)

1. G. Tzanetakis and P. Cook, "Musical genre classification of audio signals", IEEE Trans. Acoust., Speech, Signal Process., vol. 10, no. 5, pp. 293-302, Jul. 2002. (Pubitemid 34950067)
2. D. A. Reynolds and R. C. Rose, "Robust text-independent speaker identification using Gaussian mixture speaker models", IEEE Trans. Speech Audio Process., vol. 3, no. 1, pp. 72-83, Jan. 1995.
3. T. M. Cover and P. E. Hart, "Nearest neighbor pattern classification", IEEE Trans. Inf. Theory, vol. IT-13, no. 1, pp. 21-27, Jan. 1967.
4. M. I. Mandel and D. P. W. Ellis, "Song-level features and support vector machines for music classification", in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), 2005, pp. 594-599.
5. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
6. L. Lu, D. Liu, and H.-J. Zhang, "Automatic mood detection and tracking of music audio signals", IEEE Trans. Acoust., Speech, Signal Process., vol. 14, no. 1, pp. 5-18, Jan. 2006. (Pubitemid 43863448)
7. T. Li and M. Ogihara, "Content-based music similarity search and emotion detection", in Proc. Int. Conf. Music Info Retrieval (ISMIR), 2004, pp. 705-708.
8. B. Logan and A. Salomon, "Content-based music similarity function", Cambridge Research Lab, Cambridge, MA, Tech. Rep. CRL 2001/02, 2001.
9. J.-J. Aucouturier, B. Defreville, and F. Pachet, "The bag-of-frames approach to audio pattern recognition: A sufficient model for urban soundscapes but not for polyphonic music", J. Acoust. Soc. Amer., vol. 122, no. 2, pp. 881-891, 2007. (Pubitemid 47205542)
10. J.-J. Aucouturier and F. Pachet, "Representing musical genre: A state of the art", J. New Music Res., vol. 32, no. 1, pp. 83-93, 2003.
11. B. Whitman, "Learning the meaning of music", Ph. D. dissertation, Dept. of Architecture, Mass. Inst. of Tech., Cambridge, MA, 2005.
12. J.-J. Aucouturier and F. Pachet, "The influence of polyphony on the dynamical modelling of musical timbre", Pattern Recogn. Lett., vol. 28, no. 5, pp. 654-661, 2007. (Pubitemid 46073223)
13. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition", Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
14. J. P. Bello, "Grouping recorded music by structural similarity", in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), 2009, pp. 531-536.
15. A. Sheh and D. P. W. Ellis, "Chord segmentation and recognition using EM-trained hidden Markov models", in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), 2003.
16. R. Foucard, J.-L. Durrieu, M. Lagrange, and G. Richard, "Multimodal similarity between musical streams for cover version detection", in Proc. Int. Conf. Acoust., Speech, Signal Process., 2010, pp. 5514-5517.
17. K. Itoyama, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, "Instrument equalizer for query-by-example retrieval: Improving sound source separation based on integrated harmonic and inharmonic models", in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), 2008, pp. 133-138.
18. F. Pachet and D. Cazaly, "A taxonomy for musical genres", in Proc. RIAO Content-Based Multimedia Inf. Access Conf., 2000, pp. 1238-1245.
19. J.-J. Aucouturier and E. Pampalk, "Introduction-from genre to tags: A little epistemology of music information retrieval research", J. New Music Res., vol. 37, no. 2, pp. 87-92, 2008.
20. C. McKay and I. Fujinaga, "Music genre classification: Is it worth pursuing and how can it be improved?", in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), 2006, pp. 101-106.
21. J. Bennett and S. Lanning, "The Netflix prize", in Proc. KDD-Cup and Wkshp., 2007, pp. 3-6.
22. A. I. Schein and A. Daly, "Methods and metrics for cold-start recommendation", in Proc. 25th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2002, pp. 253-260.
23. J. Reed and C.-H. Lee, "A study on music genre classification based on universal acoustic models", in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), 2006, pp. 89-94.
24. M. Mohri, P. Moreno, and E. Weinstein, "Robust music identification, detection, and analysis", in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), 2007, pp. 135-138.
25. J. Reed and C.-H. Lee, "On the importance of modeling temporal information in music tag annotation", in Proc. Int. Conf. Acoust., Speech, Signal Process., 2009, pp. 1873-1876.
26. J.-M. Ren, Z.-S. Chen, and J.-S. R. Jang, "On the use of sequential patterns mining as temporal features for music genre classification", in Proc. Int. Conf. Acoust., Speech, Signal Process., 2010, pp. 2294-2297.
27. M. A. Zissman, "Comparison of four approaches to automatic language identification of telephone speech", IEEE Trans. Speech Audio Process., vol. 4, no. 1, pp. 31-44, Jan. 1997.
28. B.-H. Juang, W. Chou, and C.-H. Lee, "Minimum classification error rate methods for speech recognition", IEEE Trans. Speech Audio Process., vol. 5, no. 3, pp. 257-265, Ma. 1997. (Pubitemid 127745998)
29. S. Gao, W. Wu, C.-H. Lee, and T.-S. Chua, "A maximal figure-of-merit (MFoM) learning approach to robust classifier design for text categorization", ACM Trans. Inf. Syst., vol. 24, no. 2, pp. 190-218, 2006. (Pubitemid 44180737)
30. E. McDermott and S. Katagiri, "A derivation ofminimum classification error from the theoretical classification risk using Parzen estimation", Comput. Speech Lang., vol. 18, no. 2, pp. 107-122, 2004.
31. D. Yu, L. Deng, X. He, and A. Acero, "Large-margin minimum classification error training", Comput. Speech Lang., vol. 22, no. 4, pp. 415-429, 2008.
32. C. Raphael, "Automatic segmentationof acoustic musical signals using hidden Markov models", IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 4, pp. 360-370, Apr. 1999.
33. K. Crammer and Y. Singer, "Pranking with ranking", in Proc. Neural Inf. Process. Syst. Conf., 2001, pp. 641-647.
34. R. Herbrich, T. Graepel, and K. Obermayer, "Large margin rank boundaries for ordinal regression", in Advances in Large Margin Classifiers, A. J. Smola, P. Bartlett, B. Scholkopf, and C. Schuurmans, Eds. Cambridge, MA: MIT Press, 2000, pp. 115-132.
35. A. Shashua and A. Levin, "Ranking with large margin principles: Two approaches", in Proc. Neural Inf. Process. Syst. Conf., 2002, pp. 937-944.
36. W. Chu and S. S. Keerthi, "New approaches to support vector ordinal regression", in Proc. Int. Conf. Mach. Learn., 2005, pp. 145-152. (Pubitemid 43183327)
37. J. Bellegarda, "Exploiting latent semantic information in statistical language modeling", Proc. IEEE, vol. 88, no. 8, pp. 1279-1296, Aug. 2000.
38. T. Svendsen and F. Soong, "On the automatic segmentation of speech signals", in Proc. Int. Conf. Acoust., Speech, Signal Process., 1987, pp. 77-80.
39. S. Katagiri, B.-H. Juang, and C.-H. Lee, "Pattern recognition using a family of design algorithms based upon the generalized probabilistic descent method", Proc. IEEE, vol. 86, no. 11, pp. 2345-2373, Nov. 1998.
40. L. Xu, K. Crammer, and D. Schuurmans, "Robust support vector machine training via convex outlier ablation", Proc. Amer. Assoc. Artif. Intell., pp. 536-542, 2006. (Pubitemid 44705339)
41. A. Berenzweig, B. Logan, D. P. W. Ellis, and B. Whitman, "A largescale evaluation of acoustic and subjective music-similarity measures", Comput. Music J., vol. 28, no. 2, pp. 63-76, 2004.
42. Music User Ratings of Musical Artists, Version 1.0, Yahoo! [Online]. Available: http://research.yahoo.com
43. K. Järvelin and J. Kekäläinen, "Cumulated gain-based evaluation of IR techniques", ACM Trans. Inf. Syst., vol. 20, no. 4, pp. 422-445, 2002.
44. K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, "Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences", in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), 2006, pp. 296-301.
45. F. Wilcoxon, "Individual comparisons by ranking methods", Biometrics, pp. 80-83, 1945.