Predictability of music descriptor time series and its application to cover song detection

IEEE Transactions on Audio, Speech and Language Processing

Volumn 20, Issue 2, 2012, Pages 514-525

  Serrà, Joan ^a   Kantz, Holger ^b   Serra, Xavier ^a   Andrzejak, Ralph G ^a  

^a UNIVERSITAT POMPEU FABRA   (Spain)

^b MAX PLANCK INSTITUT FÜR PHYSIK KOMPLEXER SYSTEME   (Germany)

Author keywords

Acoustic signal analysis; information retrieval; music; prediction methods; time series

Indexed keywords

ACOUSTIC SIGNAL ANALYSIS; AUTOMATIC IDENTIFICATION; DESCRIPTORS; MUSIC; MUSICAL GENRE; MUSICAL PIECES; PREDICTION METHODS; REAL-WORLD APPLICATION; SHORT TIME INTERVALS; STATE-OF-THE-ART SYSTEM; TIME INTERVAL; TIME SERIES MODELS;

AUDIO ACOUSTICS; AUTOMATION; INFORMATION RETRIEVAL; TIME SERIES;

TIME SERIES ANALYSIS;

EID: 83755187934     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2011.2162321     Document Type: Article

References (54)

1. S. Mithen, Singing Neanderthals: The Origins of Music, Language, Mind and Body, The. Cambridge, MA: Harvard Univ. Press, 2007.
2. D. J. Levitin, "Why music moves us," Nature, vol. 464, no. 8, pp. 834-835, 2010.
3. D. Huron, Sweet Anticipation: Music and the Psychology of Expectation. Cambridge, MA: MIT Press, 2006.
4. E. Narmour, Analysis and Cognition of Basic Melodic Structures: The Implication-Realization Model, The. Chicago, IL: Univ. of Chicago Press, 1990.
5. F. Pachet, "The continuator: Musical interaction with style," J. New Music Res., vol. 31, no. 1, pp. 1-9, 2002.
6. T. Eerola, P. Toiviainen, and C. L. Krumhansl, "Real-time prediction of melodies: Continuous predictability judgements and dynamic models," in Proc. Int. Conf. Music Percept. Cognition (ICMPC), 2002, pp. 473-476.
7. S. Abdallah and M. D. Plumbey, "Information dynamics: Patterns of expectation and surprise in the perception of music," Connect. Sci., vol. 21, no. 2, pp. 89-117, 2009.
8. J. F. Paiement, Y. Grandvalet, and S. Benigo, "Predictive models for music," Connect. Sci., vol. 21, no. 2, pp. 253-272, 2009.
9. S. Dubnov, "Spectral anticipations," Comput. Music J., vol. 30, no. 2, pp. 63-83, 2006. (Pubitemid 43827279)
10. S. Dubnov, G. Assayag, and A. Cont, "Audio oracle: A new algorithm for fast learning of audio structures," in Proc. Int. Comput. Music Conf. (ICMC), 2007, pp. 224-228.
11. A. Hazan, R. Marxer, P. Brossier, H. Purwins, P. Herrera, and X. Serra, "What/when causal expectation modelling applied to audio signals," Connect. Sci., vol. 21, no. 2, pp. 119-143, 2009.
12. A. Klapuri and M. Davy, Signal Processing Methods for Music Transcription. New York: Springer Science, 2006.
13. N. Orio, "Music retrieval: A tutorial and review," Foundat. Trends in Inf. Retrieval, vol. 1, no. 1, pp. 1-90, 2006. (Pubitemid 46088691)
14. J. S. Downie, "The music information retrieval evaluation exchange (2005-2007): A window into music information retrieval research," Acoust. Sci. Technol., vol. 29, no. 4, pp. 247-255, 2008.
15. M. Casey, R. C. Veltkamp, M. Goto, M. Leman, C. Rhodes, and M. Slaney, "Content-based music information retrieval: Current directions and future challenges," Proc. IEEE, vol. 96, no. 4, pp. 668-696, Apr. 2008.
16. J. Serrà, E. Gómez, and P. Herrera, "Audio cover song identification and similarity: Background, approaches, evaluation and beyond," in Adv. in Music Information Retrieval, Z. W. Ras and A. A. Wiec-zorkowska, Eds. Berlin, Germany: Springer, 2010, vol. 16, Studies in Computational Intelligence, ch. 14, pp. 307-332.
17. D. P. W. Ellis and G. E. Poliner, "Identifying cover songs with chroma features and dynamic programming beat tracking," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2007, vol. 4, pp. 1429-1432.
18. M. Marolt, "A mid-level representation for melody-based retrieval in audio collections," IEEE Trans. Multimedia, vol. 10, no. 8, pp. 1617-1625, Dec. 2008.
19. J. Serrà, E. Gómez, P. Herrera, and X. Serra, "Chroma binary similarity and local alignment applied to cover song identification, " IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 6, pp. 1138-1152, Aug. 2008.
20. J. Serrà, X. Serra, and R. G. Andrzejak, "Cross recurrence quantification for cover song identification," New J. Phys., vol. 11, p. 093017, 2009.
21. L. R. Rabiner and B. H. Juang, Fundamentals of Speech Recognition. Upper Saddle River, NJ: Prentice-Hall, 1993.
22. J. Serrà, E. Gómez, and P. Herrera, "Transposing chroma representations to a common key," in Proc. IEEE CS Conf. Use of Symbols to Represent Music and Multimedia Objects, 2008, pp. 45-48.
23. E. Gómez, P. Herrera, P. Cano, J. Janer, J. Serrà, J. Bonada, S. El-Hajj, T. Aussenac, and G. Holmberg, "Music similarity systems and methods using descriptors," U.S. Patent 2008/0300702, Dec. 4, 2008.
24. E. Gómez, "Tonal description of music audio signals," Ph.D. dissertation, Univ. Pompeu Fabra, Barcelona, Spain, 2006.
25. C. Harte, M. B. Sandler, and M. Gasser, "Detecting harmonic change in musical audio," in Proc. ACM Workshop Audio Music Comput. Multimedia, 2006, pp. 21-26. (Pubitemid 47168287)
26. M. Slaney, "Auditory Toolbox," Tech. Rep. 1998 [Online]. Available: http://cobweb.ecn.purdue.edu/malcolm/interval/1998-010, 2
27. V. Akkermans, J. Serrà, and P. Herrera, "Shape-based spectral contrast descriptor," in Proc. Sound Music Comput. Conf. (SMC), 2009, pp. 143-148.
28. P. Brossier, "Automatic annotation of musical audio for interactive applications," Ph.D., Queen Mary, Univ. of London, London, U.K., 2007.
29. E. Guaus and P. Herrera, "The rhythm transform: Towards a generic rhythm description," in Proc. Int. Comput. Music Conf. (ICMC), 2005, 1131.
30. T. Pohle, D. Schnitzer, M. Schedl, P. Knees, and G. Widmer, "On rhythm and general music similarity," in Proc. Int. Soc. Music Inf. Retrieval Conf. (ISMIR), 2009, pp. 525-530.
31. P. Desain and H. Honing, "The formation of rhythmic categories and metric priming," Perception, vol. 32, no. 3, pp. 341-365, 2003. (Pubitemid 37551059)
32. A. V. Oppenheim, R. W. Schafer, and J. B. Buck, Discrete-Time Signal Processing, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999.
33. E. Vickers, "Automatic long-term loudness and dynamics matching," in Proc. Conv. Audio Eng. Soc. (AES), 2001, 5495.
34. H. Kantz and T. Schreiber, Nonlinear Time Series Analysis, 2nd ed. Cambridge, U.K.: Cambridge Univ. Press, 2004.
35. F. Takens, "Detecting strange attractors in turbulence," Lecture Notes in Math., vol. 898, pp. 366-381, 1981.
36. I. Mierswa and K. Morik, "Automatic feature extraction for classifying audio data," Mach. Learn. J., vol. 58, pp. 127-149, 2005. (Pubitemid 40400635)
37. F. Mörchen, A. Ultsch, M. Thies, and I. Löhken, "Modelling timbre distance with temporal statistics from polyphonic music," IEEE Trans. Speech Audio Process., vol. 14, no. 1, pp. 81-90, Jan. 2006. (Pubitemid 43863455)
38. G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control, rev. ed. Oakland, CA: Holden-Day, 1976.
39. H. Lütkepohl, New Introduction to Multiple Time Series Analysis. Berlin, Germany: Springer, 2005.
40. A. S. Weigend and N. A. Gershenfeld, Time Series Prediction: Forecasting the Future and Understanding the Past. Boulder, CO: West-view Press, 1993.
41. N. G. Van Kampen, Stochastic Processes in Physics and Chemistry, 3rd ed. Amsterdam, The Netherlands: Elsevier, 2007.
42. H. S. Parka and C. S. Jun, "A simple and fast algorithm for k-medoids clustering," Expert Syst. Applicat., vol. 36, no. 2, pp. 3336-3341, 2009.
43. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 3rd ed. San Diego, CA: Academic, 2006.
44. W. H. Press, B. P. Flannery, S. A. Tenkolsky, and W. T. Vetterling, Numerical Recipes, 2nd ed. Cambridge, U.K.: Cambridge Univ. Press, 1992.
45. A. Meng, P. Ahrendt, J. Larsen, and L. K. Hansen, "Temporal feature integration for music genre classification," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 5, pp. 1654-1664, Jul. 2007.
46. C. Joder, S. Essid, and G. Richard, "Temporal integration for audio classification with application to musical instrument classification," IEEE Trans. Audio, Speech, Lang. Process., vol. 17, no. 1, pp. 174-186, Jan. 2009.
47. H. Tong and K. S. Lim, "Threshold autoregression, limit cycles and cyclical data," J. R. Statist. Soc, vol. 42, no. 3, pp. 245-292, 1980.
48. D. S. Broomhead and D. Lowe, "Multivariable functional interpolation and adaptive networks," Complex Syst., vol. 2, pp. 321-355, 1988.
49. J. D. Farmer and J. J. Sidorowich, "Predicting chaotic time series," Phys. Rev. Lett., vol. 59, no. 8, pp. 845-848, 1987.
50. C. D. Manning, R. Prabhakar, and H. Schutze, An Introduction to Information Retrieval. Cambridge, U.K.: Cambridge Univ. Press, 2008.
51. C. P. Robert and G. Casella, Monte Carlo Statistical Methods, 2nd ed. Berlin, Germany: Springer, 2004.
52. T. Schreiber and A. Schmidt, "Surrogate time series," Physica D, vol. 142, pp. 346-382, 2000.
53. O. Cappé, E. Moulines, and T. Rydén, Inference in Hidden Markov Models. New York: Springer Science, 2005.
54. E. Batlle, J. Masip, and E. Guaus, "Automatic song identification in noisy broadcast audio," in Proc. Signal Image Process. Conf. (SIP), 2002, pp. 101-111.