Spectral and Temporal Periodicity Representations of Rhythm for the Automatic Classification of Music Audio Signal

IEEE Transactions on Audio, Speech and Language Processing

Volumn 19, Issue 5, 2011, Pages 1242-1252

  Peeters, Geoffroy ^a  

^a Université Pierre et Marie Curie   (France)

Author keywords

Audio features; automatic indexing; rhythm classification; rhythm description

Indexed keywords

EID: 85008023442     PISSN: 15587916     EISSN: 15587924     Source Type: Journal    
DOI: 10.1109/TASL.2010.2089452     Document Type: Article

References (31)

1. G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Trans. Speech Audio Process., vol. 10, no. 5, pp. 293–302, Jul. 2002.
2. J. Foote and S. Uchihashi, “The beat spectrum: A new approach to rhythm analysis,” in Proc. ICME (IEEE Int. Conf. Multimedia Expo), 2001, pp. 1088–1091.
3. J. Foote, M. Cooper, and U. Nam, “Audio retrieval by rhythmic similarity,” in Proc. ISMIR, Paris, France, 2002, pp. 265–266.
4. I. Antonopoulos, A. Pikrakis, S. Theodoridis, O. Cornelis, D. Moe-lants, and M. Leman, “Music retrieval by rhythmic similarity applied on greek and African traditional music,” in Proc. ISMIR, Vienna, Austria, 2007, pp. 297–300.
5. J. Paulus and A. Klapuri, “Measuring the similarity of rhythmic patterns,” in Proc. ISMIR, Paris, France, 2002, pp. 150–156.
6. F. Gouyon, S. Dixon, E. Pampalk, and G. Widmer, “Evaluating rhythmic descriptors for musical genre classification,” in Proc. AES 25th Int. Conf. Metadata for Audio, London, U.K., 2004, pp. 196–204.
7. E. Pampalk, S. Dixon, and G. Widmer, “Exploring music collections by browsing different views,” in Proc. ISMIR, Baltimore, MD, 2003, pp. 201–208.
8. F. Gouyon and S. Dixon, “Dance music classification: A tempo-based approach,” in Proc. ISMIR, Barcelona, Spain, 2004, pp. 501–504.
9. S. Dixon, F. Gouyon, and G. Widmer, “Towards characterisation of music via rhythmic patterns,” in Proc. ISMIR, Barcelona, Spain, 2004, pp. 509–516.
10. M. Wright, W. Schloss, and G. Tzanetakis, “Analyzing afro-cuban rhythms using rotation-aware cave template matching with dynamic programming,” in Proc. ISMIR, Philadelphia, PA, 2008, pp. 647–652.
11. A. Holzapfel and Y. Stylianou, “Rhythmic similarity of music based on dynamic periodicity warping,” in Proc. IEEE ICASSP, Las Vegas, NV, 2008, pp. 2217–2220.
12. A. Holzapfel and Y. Stylianou, “A scale transform based method for rhythmic similarity of music,” in Proc. IEEE ICASSP, Taipei, Taiwan, 2009, pp. 317–320.
13. J. Jensen, M. Christensen, and S. Jensen, “A tempo-insensitive representation of rhythmic patterns,” in Proc. Eusipco, Glasgow, U.K., 2009.
14. M. Gruhne, C. Dittmar, and D. Gaertner, “Improving rhythmic similarity computation by beat histogram transformations,” in Proc. ISMIR, Kobe, Japan, 2009.
15. C. Uhle and C. Dittmar, “Drum pattern based genre classification of popular music,” in Proc. AES 25th Int. Conf. Metadata for Audio, London, U.K., 2004.
16. E. Tsunoo, G. Tzanetakis, N. Ono, and S. Sagayama, “Audio genre classification using percussive pattern clustering combined with tim-bral features,” in Proc. ICME (Int. Conf. Multimedia Expo), 2009, pp. 382–385.
17. N. Ono, K. Miyamoto, H. Kameoka, and S. Sagayama, “A real-time equalizer of harmonic and percussive components in music signals,” in Proc. ISMIR, Philadelphia, PA, 2008.
18. G. Peeters “Template-based estimation of time-varying tempo,” EURASIP J. Appl. Signal Process., vol. 2007, no. 1, pp. 158-158, 2007.
19. G. Peeters, “Music pitch representation by periodicity measures based on combined temporal and spectral representations,” in Proc. IEEE ICASSP, Toulouse, France, 2006, vol. V, pp. 53–56.
20. G. Peeters and X. Rodet, D. World, Ed., “Non-stationary analysis/synthesis using spectrum peak shape distortion, phase and reassignement,” in Proc. ICSPAT — DSP World, Orlando, FL, 1999.
21. J. Seppanen, A. Eronen, and J. Hiipakka, “Joint beat and tatum tracking from music signals,” in Proc. ISMIR, Victoria, BC, Canada, 2006.
22. J. R. Quinlan, C4.5.: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
23. E. Frank and I. Witten, “Generating accurate rule sets without global optimization,” in Proc. ICML (Int. Conf. Mach. Learn.), 1998, pp. 144–151.
24. E. Frank, Y. Wang, S. Inglis, G. Holmes, and I. Witten “Using model trees for classification,” Mach. Learn., vol. 32, no. 1, pp. 63–76, 1998.
25. C. Burges “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, pp. 121–127, 1998.
26. Y. Freund and R. Schapir “A decision-theoretic generalization of on-line learning and an application to boosting,” J. Comput. Syst. Sci., vol. 55, pp. 119–139, 1997.
27. L. Breiman “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
28. I. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques With Java Implementations. San Francisco, CA: Morgan Kaufmann, 1999.
29. G. Peeters and X. Rodet, “Hierachical gaussian tree with inertia ratiomaximization for the classification of large musical instrument database,” in Proc. DAFX, London, U.K., 2003, pp. 318–323, peeters03c.
30. M. Hall, “Feature selection for discrete and numeric class machine learning,” Tech. Rep., 1999.
31. J. Brown and M. Puckette “Calculation of a “narrowed” autocorrelation function,” J. Acoust. Soc. Amer., vol. 85, no. 4, pp. 1595–1601, 1989.