Improving multilabel analysis of music titles: A large-scale validation of the correction approach

IEEE Transactions on Audio, Speech and Language Processing

Volumn 17, Issue 2, 2009, Pages 335-343

  Pachet, François ^a   Roy, Pierre ^a  

^a SONY COMPUTER SCIENCE LABORATORY   (France)

Author keywords

Feature extraction; Learning systems; Music; Pattern classification

Indexed keywords

ACOUSTIC SIGNALS; AUDIO FEATURES; CLASSIFIER FUSION; CORRECTION SCHEMES; LARGE-SCALE DATABASE; MULTI-LABEL; MUSIC; PATTERN CLASSIFICATION; SCALE VALIDATION; SUPERVISED CLASSIFICATION;

AUDIO ACOUSTICS; CLASSIFIERS; LABELS; LEARNING SYSTEMS; METADATA;

FEATURE EXTRACTION;

EID: 70350446810     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2008.2008734     Document Type: Article

References (31)

1. C. Anderson, The Long Tail: Why the Future of Business is Selling Less of More Rev, Updated ed. New York: Hyperion, 2008.
2. G.Widmer, S. Dixon, P. Knees, E. Pampalk, and T. Pohle, "From sound to sense via feature extraction and machine learning: Deriving highlevel descriptors for characterising music, " in Sound to Sense, Sense to Sound-A State of the Art in Sound and Music Computing, P. Polotti and D. Rocchesso, Eds. Berlin, Germany: Logos Verlag, 2007.
3. F. Pachet, "Knowledge management and musical metadata, " Encyclopedia of Knowledge Management Idea Group, 2005.
4. U. Shardanand and P. Maes, "Social information filtering: algorithms for automating word of mouth, " in Proc. CHI, 1995, pp. 210-217.
5. J.-J. Aucouturier and F. Pachet, "Improving timbre similarity: How high is the sky?, " J. Negative Results Speech Audio Sci., vol. 1, no. 1, 2004.
6. E. Pampalk, A. Flexer, and G.Widmer, "Improvements of audio-based music similarity and genre classification, " in Proc. ISMIR, London, U.K., 2005, pp. 628-633.
7. E. K. Youngmoo and B. Whitman, "Singer identification in popular music recordings using voice coding features, " in Proc. ISMIR, Paris, France, 2002, pp. 329-336.
8. E. G. Richard and B. David, "Instrument recognition in polyphonic music based on automatic taxonomies, " IEEE Trans. Speech, Audio, Lang. Process., vol. 14, no. 1, pp. 68-80, Jan. 2006.
9. D. Liu, L. Lu, and H.-J. Zhang, "Automatic mood detection and tracking of music audio signals, " IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 1, pp. 5-18, Jan. 2006.
10. J.-J. Aucouturier and F. Pachet, "A scale-free distribution of false positives for a large class of audio similarity measures, " Pattern Recognition, vol. 41, no. 1, pp. 272-284, 2007.
11. H. G. Kim, N. Moreau, and T. Sikora, MPEG7 Audio and Beyond: Audio Content Indexing and Retrieval. New York: Wiley, 2005.
12. M. Alonso, B. David, and G. Richard, "A study of tempo tracking algorithms from polyphonic music signals, " in Proc. 4th COST 276 Workshop, France, Mar. 2003.
13. E. Narmour, The Analysis and Cognition of Basic Melodic Structures. Chicago, IL: Chicago Univ. Press, 1990.
14. M. Goto, H. Hashigushi, T. Nishimura, and R. Oka, "RWCmusic database: Popular, classical and jazz music databases, " in Proc. ISMIR, Paris, France, 2002, pp. 287-288.
15. P. Cano, E. Gómez, F. Gouyon, P. Herrera, M. Koppenberger, B. Ong, X. Serra, S. Streich, and N.Wack, "ISMIR 2004 audio description contest, " MTG Tech. Rep. MTG-TR-2006-02, 2006.
16. P. Rabbat and F. Pachet, "Statistical inference in large-scale databases: How to make a title funk?, " in Proc. ISMIR, Philadelphia, PA, 2008, pp. 589-594.
17. J.-J. Aucouturier, F. Pachet, P. Roy, and A. Beurivé, "Signal + context = better classification, " in Proc. ISMIR, Vienna, Austria, 2007, pp. 425-430.
18. Y. S. Huang and C. Y. Suen, "A method of combining multiple experts for the recognition of unconstrained handwritten numerals, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 1, pp. 90-94, Jan. 1995.
19. T. K. Ho, "Multiple classifier combination: Lessons and next steps, " in Kandel, Bunke, Hybrid Methods in Pattern Recognition. Singapore: World Scientific, 2002, pp. 171-198.
20. S.-B. Cho and J. H. Kim, "Combining multiple neural networks by fuzzy integral and robust classification, " IEEE Trans. Syst, Man, Cybern., vol. 25, no. 2, pp. 380-384, Feb. 1995.
21. Y. Lu, "Knowledge integration in a multiple classifier system, " Appl. Intell., vol. 6, pp. 75-86, 1996.
22. D. H. Wolpert, "Stacked generalization, " Neural Netw., vol. 5, no. 2, pp. 241-260, 1992.
23. J.-J. Aucouturier, "Ten experiments on the modeling of polyphonic timbre, " Ph.D. dissertation, Univ. Pierre et Marie Curie, Paris, France, 2006.
24. R. Campana, "Organisation et catégorisation de la musique populaire par apprentissage statistique, " Sony CSL, Paris, France, 2006, Tech. Rep. CSLP-TR-06-01.
25. R. H. Frank and P. J. Cook, The Winner-Take-All Society. New York: Free Press, 1995.
26. M. J. Salganik, P. S. Dodds, and D. J. Watts, "Experimental study of inequality and unpredictability in an artificial cultural market, " Science, vol. 311, pp. 854-856, Feb. 2006.
27. R. Dhanaraj and B. Logan, "Automatic prediction of hit songs, " in Proc. of ISMIR 2005, London, UK, 2005.
28. M. G. Gladwell, "The formula, The NewYorker, " 2006 [Online].Available: http://www.gladwell.com/2006/2006-10-16-a-formula.html.
29. "PlatiniumBlue." [Online]. Available: http://www. platinumblueinc.com.
30. "HitSongScience." [Online]. Available: http://www. hitsongscience.com.
31. I. H. Witten and F. Eibe, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. San Francisco, CA: Morgan Kaufmann, 2005.