Score-independent audio features for description of music expression

IEEE Transactions on Audio, Speech and Language Processing

Volumn 16, Issue 2, 2008, Pages 458-466

  Mion, Luca ^a   De Poli, Giovanni ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Audio classification; Expression communication; Music understanding

Indexed keywords

ACOUSTIC PARAMETERS; AUDIO CLASSIFICATION; AUDIO DATUM; AUDIO FEATURES; EXPRESSION COMMUNICATION; GENERAL DESCRIPTIONS; INTERMEDIATE LEVELS; MACHINE-LEARNING TECHNIQUES; MUSIC INFORMATION RETRIEVALS; MUSIC PERFORMANCE; MUSIC UNDERSTANDING; MUSICAL EVENTS; PHYSICAL LEVELS; PHYSICAL METAPHORS; SEQUENTIAL FORWARD SELECTIONS; STRUCTURED LANGUAGES;

ELECTRONIC MUSICAL INSTRUMENTS; FEATURE EXTRACTION; INFORMATION RETRIEVAL; INFORMATION SERVICES; LEARNING ALGORITHMS; LEARNING SYSTEMS; SEARCH ENGINES;

AUDIO ACOUSTICS;

EID: 39649089103     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2007.913743     Document Type: Article

References (34)

1. P. Desain and H. Honing, "Towards a calculus for expressive timing in music," Comput. Music Res., vol. 3, pp. 43-120, 1991.
2. A. Friberg, V. Colombo, L. Frydén, and J. Sundberg, "Generating musical performances with director musices," Comput. Music J., vol. 24, no. 3, pp. 23-29, 2000.
3. S. Canazza, G. De Poli, C. Drioli, A. Roda, and A. Vidolin, "Audio morphing different expressive intentions for multimedia systems," IEEE Trans. Multimedia, vol. 7, no. 3, pp. 79-83, Jul./Sep. 2000.
4. R. Hiraga. H. Katayose, H. Koike, T. Suzuki, K. Noike, and T. Hoshishiba, "Performance rendering 2000: Demonstration and panel discussion," in Proc. 2000-MUS-35, IPSJ, 2000, pp. 67-70.
5. M. Clynes, "Some guidelines for the syndiesis and testing of pulse mi- crostructure in relation to musical meaning," Music Perception, vol. 7, no. 4, pp. 403-422, 1990.
6. N. P. Todd, "The dynamics of dynamics: A model of musical expression," /. Acoust. Soc. Amer., vol. 91, no. 6, pp. 3540-3550, 1992.
7. G. De Poli, A. Roda, and A. Vidolin, "Note-by-note analysis of the influence of expressive intentions and musical structure in violin performance," J. New Music Res., vol. 27, no. 3, pp. 293-321, 1998.
8. R. Bresin and A. Friberg, "Emotional coloring of computer controlled music performance," Comput. Music J., vol. 24, no. 4, pp. 44-63, 2000.
9. E. Bigand, S. Filipic, and P. Lalitte, "The time course of emotional responses to music," Ann. New York Acad. Sci, vol. 1060, no. 1, pp. 429-137, 2005.
10. P. N. Juslin, "Communicating emotion in music performance: A review and a theoretical framework," in Music and Emotion: Theory and Research, P. N. Juslin and J. A. Sloboda, Eds. New York: Oxford Univ. Press, 2001, pp. 305-333.
11. A. Friberg, E. Schoonderwaldt, P. Juslin, and R. Bresin, "Automatic real-time extraction of musical expression," in Proc. Int. Comput. Music Conf. (ICMC02), Goteborg, Sweden, 2002, pp. 365-367.
12. G. Widmer and W. Goebl, "Computational models of expressive music performance: The state of the art," /. New Music Res., vol. 33, pp. 211-224, 2004.
13. R. Dannenberg, B. Thorn, and D. Watson, "A machine learning approach to musical style recognition," in Proc. Int. Comput. Music Conf. (ICMC97), San Francisco, CA, 1997, pp. 344-347.
14. L. Mion, "Application of bayesian networks to automatic recognition of expressive content of piano improvisations," in Proc. Stockholm Music Acoust. Conf. (SMAC'03), Stockholm. Sweden. Aug. 2003, pp. 557-560.
15. L. Lu, D. Liu, 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.
16. E. Cambouropoulos, "The local boundary detection model (Ibdm) and its application in the study of expressive timing," in Proc. Int. Comput. Music Conf. (ICMC01), Havana, Cuba, 2001, pp. 290-293.
17. F. Bonini, A. Rodd, and G. De Poli, "Communicating expressive intentions with single piano notes," J. New Music Res., vol. 35, no. 3, pp. 197-210, 2006.
18. R. Bresin, "Artificial neural networks based models for automatic performance of musical scores," J. New Music Res., vol. 27, no. 3, pp. 239-270, 1998.
19. S. Canazza, G. De Poli, C. Drioli, A. Roda, and A. Vidolin, "Modeling and control of expressiveness in music performance," Proc. IEEE, vol. 92, no. 4, pp. 286-701, Apr. 2004.
20. A. Camurri, G. De Poli, M. Leman, and G. Volpe, "Communicating expressiveness and affect in multimodal interactive systems," IEEE Multimedia, vol. 12, no. 1, pp. 43-53, Jan./Mar. 2005.
21. L. Mion and G. DTncd, "Analysis of expression in simple musical gestures to enhance audio in interfaces," Virtual Reality, vol. 10, no. 1, pp. 62-70, 2006.
22. J. A. Russell, "A circumplex model of affect," J. Personality Social Psychol, vol. 39, pp. 1161-1178, 1980.
23. P. N. Juslin and J. A. Sloboda, Music and Emotion: Theory and Research. New York: Oxford Univ. Press, 2001.
24. S. Canazza, G. De Poli, A. Roda, and A. Vidolin, "An abstract control space for communication of sensory expressive intentions in music performance," J. New Music Res., vol. 32, no. 3, pp. 281-294, 2003.
25. M. Leman, "Visualization and calculation of roughness of acoustical musical signals using the synchronization index model (SIM)," in Proc. COSTG-6 Conf. Digital Audio Effects (DAFX-OO), Verona, Italy, 2000, pp. 125-130.
26. L. V. lmmerseel and J. Martens, "Pitch and voiced/unvoiced determination with an auditory model," /. Acoust. Soc. Amer., vol. 91, pp. 3511-3526, 1992.
27. U. Zoelzer, Ed., DA FX: Digital Audio Effects. New York: Wiley, 2002.
28. X. Sena, "Musical sound modeling with sinusoids plus noise," in Musical Signal Process., C. Roads, S. T. Pope, A. Piccialli, and G. De Poli, Eds. Lisse, The Netherlands: Swets & Zeitlinger. 1997, pp. 91-122.
29. N. Laurenti, G. De Poli, and D. Montagner, "A nonlinear method for stochastic spectrum estimation in the modeling of musical sounds," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 2, pp. 531-541, Feb. 2007.
30. A. Whitney, "A direct method of nonparametric measurement selection," IEEE Trans. Comput., vol. C-20, no. 9, pp. 1100-1103, Sep. 1971.
31. R. Duda, P. Hart, and D. Stork, Pattern Classification, 2nd ed. New York: Wiley, 2001.
32. C. Cortes and V. Vapnik, "Support vector networks," Mach. Learn., vol. 20, pp. 273-295, 1995.
33. M. Stone, "Cross-validatory choice and assessment of statistical predictions (with discussion)," J. R. Statist. Soc. B. vol. 36, pp. 111-147, 1974.
34. C. McKay and I. Fujinaga, "Automatic genre classification using large high-level musical feature sets," in Proc.5th Int. Conf. Music Inf. Retrieval (ISMIR 2004), Barcelona, Spain, 2004, pp. 525-530.