Advertisement

Musical Virtuosity and Creativity

  • François Pachet

Abstract

Virtuosos are human beings who exhibit exceptional performance in their field of activity. In particular, virtuosos are interesting for creativity studies because they are exceptional problem solvers. However, virtuosity is an under-studied field of human behaviour. Little is known about the processes involved to become a virtuoso, and in how they distinguish themselves from normal performers. Virtuosos exist in virtually all domains of human activities, and we focus in this chapter on the specific case of virtuosity in jazz improvisation. We first introduce some facts about virtuosos coming from physiology, and then focus on the case of jazz. Automatic generation of improvisation has long been a subject of study for computer science, and many techniques have been proposed to generate music improvisation in various genres. The jazz style in particular abounds with programs that create improvisations of a reasonable level. However, no approach so far exhibits virtuoso-level performance. We describe an architecture for the generation of virtuoso bebop phrases which integrates novel music generation mechanisms in a principled way. We argue that modelling such outstanding phenomena can contribute substantially to the understanding of creativity in humans and machines.

Keywords

Constraint Satisfaction Problem Pitch Contour Linear Improvisation Chord Sequence Markov Generation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Addessi, A., & Pachet, F. (2005). Experiments with a musical machine: musical style replication in 3/5 year old children. British Journal of Music Education, 22(1), 21–46. CrossRefGoogle Scholar
  2. Assayag, G., & Dubnov, S. (2004). Using factor oracles for machine improvisation. Soft Computing, 8(9). Google Scholar
  3. Bäckman, K., & Dahlstedt, P. (2008). A generative representation for the evolution of jazz solos. In EvoWorkshops 2008 (Vol. 4974, pp. 371–380). Napoli: Springer. Google Scholar
  4. Baggi, D. (2001). Capire il jazz, le strutture dello swing. Istituto CIM della Svizzera Italiana. Google Scholar
  5. Baker, D. (2000). Bebop characteristics. Aebersold Jazz Inc. Google Scholar
  6. Bensch, S., & Hasselquist, D. (1991). Evidence for active female choice in a polygynous warbler. Animal Behavior, 44, 301–311. CrossRefGoogle Scholar
  7. Biles, J. (1994). Genjam: a genetic algorithm for generating jazz solos. In Proc. of ICMC, Aarhus, Denmark, ICMA. Google Scholar
  8. Bresin, R. (2000). Virtual virtuosity, studies in automatic music performance. PhD thesis, KTH, Stockholm, Sweden. Google Scholar
  9. Brooks, F. P. Jr., Hopkins, A. L. Jr., Neumann, P. G., & Wright, W. V. (1957). An experiment in musical composition. IRE Transactions on Electronic Computers, 6(1). Google Scholar
  10. Cappellini, G., Ivanenko, Y. P., Poppele, R. E., & Lacquaniti, F. (2006). Motor patterns in human walking and running. Journal of Neurophysiology, 95, 3426–3437. CrossRefGoogle Scholar
  11. Chordia, P., Sastry, A., Mallikarjuna, T., & Albin, A. (2010). Multiple viewpoints modeling of tabla sequences. In Proc. of int. symp. on music information retrieval, Utrecht (pp. 381–386). Google Scholar
  12. Coker, J. (1984). Jazz keyboard for pianists and non-pianists. Van Nuys: Alfred Publishing. Google Scholar
  13. Coker, J. (1997). Complete method for improvisation (revised ed.). Van Nuys: Alfred Publishing. Google Scholar
  14. Conklin, D. (2003). Music generation from statistical models. In Proceedings of symposium on AI and creativity in the arts and sciences (pp. 30–35). Google Scholar
  15. Conklin, D., & Witten, I. (1995). Multiple viewpoint systems for music prediction. Journal of New Music Research, 24, 51–73. CrossRefGoogle Scholar
  16. Cont, A., Dubnov, S., & Assayag, G. (2007). Anticipatory model of musical style imitation using collaborative and competitive reinforcement learning. LNCS (Vol. 4520, pp. 285–306). Berlin: Springer. Google Scholar
  17. Cope, D. (1996). Experiments in musical intelligence. Madison: A-R Editions. Google Scholar
  18. Draganoiu, T. I., Nagle, L., & Kreutzer, M. (2002). Directional female preference for an exaggerated male trait in canary (serinus canaria) song. Proceedings of the Royal Society of London B, 269, 2525–2531. CrossRefGoogle Scholar
  19. Ericsson, K., Krampe, R., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363–406. CrossRefGoogle Scholar
  20. Fitt, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391. CrossRefGoogle Scholar
  21. Franklin, J. A. (2006). Recurrent neural networks for music computation. INFORMS Journal on Computing, 18(3), 321–338. CrossRefGoogle Scholar
  22. Freuder, E. & Mackworth, A. (Eds.) (1994). Constraint-based reasoning. Cambridge: MIT Press. zbMATHGoogle Scholar
  23. Gladwell, M. (2008). Outliers, the story of success. London: Allen Lane. Google Scholar
  24. Grachten, M. (2001). Jig: jazz improvisation generator. In Workshop on current research directions in computer music, Audiovisual Institute-UPF (pp. 1–6). Google Scholar
  25. GuitarTrio (1977). Friday night in San Francisco, choruses by Al Di Meola, John McLaughlin and Paco De Lucia. Artist transcriptions series. Milwaukee: Hal Leonard. Google Scholar
  26. Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4, 11–26. CrossRefGoogle Scholar
  27. Hiller, L., & Isaacson, L. (1958). Musical composition with a high-speed digital computer. Journal of the Audio Engineering Society, 6(3), 154–160. Google Scholar
  28. Hodgson, P. W. (2006). Learning and the evolution of melodic complexity in virtuoso jazz improvisation. In Proc. of the cognitive science society conference, Vancouver. Google Scholar
  29. Holbrook, M. B. (2009). Playing the changes on the jazz metaphor: an expanded conceptualization of music, management and marketing related themes. Foundations and Trends in Marketing, 2(3–4), 185–442. Google Scholar
  30. Howard, V. A. (2008). Charm and speed virtuosity in the performing arts. New York: Peter Lang. Google Scholar
  31. Johnson-Laird, P. N. (1991). Jazz improvisation: a theory at the computational level. In P. Howell, R. West & I. Cross (Eds.), Representing musical structure. San Diego: Academic Press. Google Scholar
  32. Johnson-Laird, P. N. (2002). How jazz musicians improvise. Music Perception, 19(3), 415–442. CrossRefGoogle Scholar
  33. Keller, B., Jones, S., Thom, B., & Wolin, A. (2005). An interactive tool for learning improvisation through composition (Technical Report HMC-CS-2005-02). Harvey Mudd College. Google Scholar
  34. Keller, R. M., & Morrison, D. R. (2007). A grammatical approach to automatic improvisation. In Proc. SMC 07, Lefkada, Greece. Google Scholar
  35. Krumhansl, C. (1990). Cognitive foundations of musical pitch. New York: Oxford University Press. Google Scholar
  36. Lemaire, A., & Rousseaux, F. (2009). Hypercalculia for the mind emulation. AI & Society, 24(2), 191–196. CrossRefGoogle Scholar
  37. Lenat, D. B., & Feigenbaum, E. A. (1991). On the thresholds of knowledge. Artificial Intelligence, 47(1–3), 185–250. MathSciNetCrossRefGoogle Scholar
  38. Levine, M. (1995). The jazz theory book. Petaluma: Sher Music Company. Google Scholar
  39. London, J. (2004). Hearing in time. New York: Oxford University Press. CrossRefGoogle Scholar
  40. London, J. (2010). The rules of the game: cognitive constraints on musical virtuosity and musical humor. In Course at interdisciplinary, college (IK), Lake Möhne, Germany. Google Scholar
  41. Martino, P. (1994). Creative force, Part II. Miami: CPP Media/Belwin. Google Scholar
  42. McCorduck, P. (1991). AARON’s code. New York: Freeman. Google Scholar
  43. McLaughlin, J. (2004). This is the way I do it. In The ultimate guitar workshop on improvisation, Mediastarz, Monaco. 3 DVD set. Google Scholar
  44. Nierhaus, G. (2009). Algorithmic composition, paradigms of automated music generation. Berlin: Springer. zbMATHGoogle Scholar
  45. O’Dea, J. (2000). Virtue or virtuosity? Wesport: Greenwood Press. Google Scholar
  46. OuLiPo (1988). Atlas de littérature potentielle. Gallimard: Folio/Essais. Google Scholar
  47. Pachet, F. (2003). The continuator: musical interaction with style. Journal of New Music Research, 32(3), 333–341. CrossRefGoogle Scholar
  48. Pachet, F., & Roy, P. (2011). Markov constraints: steerable generation of Markov sequences. Constraints, 16(2). Google Scholar
  49. Papadopoulos, G., & Wiggins, G. (1998). A genetic algorithm for the generation of jazz melodies. In Proceedings of STeP’98, Jyvaskyla, Finland. Google Scholar
  50. Penesco, A. (1997). Défense et illustration de la virtuosité. Lyon: Presses Universitaires de Lyon. Google Scholar
  51. Ramalho, G. (1997). Un agent rationnel jouant du jazz. PhD thesis, University of Paris 6. http://www.di.ufpe.br/~glr/Thesis/thesis-final.pdf.
  52. Ramalho, G., & Ganascia, J.-G. (1994). Simulating creativity in jazz performance. In Proc. of the 12th national conference on artificial intelligence, AAAI’94 (pp. 108–113). Seattle: AAAI Press. Google Scholar
  53. Ramirez, R., Hazan, A., Maestre, E., & Serra, X. (2008). A genetic rule-based model of expressive performance for jazz saxophone. Computer Music Journal, 32(1), 38–50. CrossRefGoogle Scholar
  54. Real (1981). The real book. The Real Book Press. Google Scholar
  55. Ricker, R. (1997). New concepts in linear improvisation. Miami: Warner Bros Publications. Google Scholar
  56. Ron, D., Singer, Y, & Tishby, N. (1996). The power of amnesia: learning probabilistic automata with variable memory length. Machine Learning, 25(2–3), 117–149. zbMATHCrossRefGoogle Scholar
  57. Shim, E. (2007). Lennie tristano, his life in music (p. 183). Ann Arbor: University of Michigan Press. Google Scholar
  58. Sloboda, J., Davidson, J., Howe, M., & Moore, D. (1996). The role of practice in the development of performing musicians. British Journal of Psychology, 87, 287–309. CrossRefGoogle Scholar
  59. Steedman, M. J. (1984). A generative grammar for jazz chord sequences. Music Perception, 2(1), 52–77. CrossRefGoogle Scholar
  60. Steen, J. (2008). Verse and virtuosity, the adaptation of Latin rhetoric in old English poetry. Toronto: University of Toronto Press. Google Scholar
  61. Stein, L. A. (1992). Resolving ambiguity in nonmonotonic inheritance hierarchies. Artificial Intelligence, 55, 259–310. MathSciNetzbMATHCrossRefGoogle Scholar
  62. Sudnow, D. (1978). Ways of the hand. London: Routledge & Kegan Paul. Google Scholar
  63. Thom, B. (2000). Bob: an interactive improvisational music companion. In Proc. of the fourth international conference on autonomous agents, Barcelona, Catalonia, Spain (pp. 309–316). New York: ACM Press. CrossRefGoogle Scholar
  64. Ulrich, J. W. (1977). The analysis and synthesis of jazz by computer. In Proc. of IJCAI (pp. 865–872). Google Scholar
  65. Valéry, P. (1948). Esquisse d’un éloge de la virtuosité. In La table ronde (pp. 387–392). Google Scholar
  66. Van Tonder, G. J., Lyons, M. J., & Ejima, Y. (2002). Perception psychology: visual structure of a Japanese zen garden. Nature, 419(6905), 359–360. CrossRefGoogle Scholar
  67. Van Veenendaal, A. (2004). Continuator plays the improvisation Turing test. http://www.csl.sony.fr/~pachet/video_vaeenendalcontinuator.html.
  68. Virtuoso (2011). Accompanying website. www.csl.sony.fr/Virtuoso.
  69. Voss, R. F., & Clarke, J. (1978). 1/f noise in music: music from 1/f noise. The Journal of the Acoustical Society of America, 63(1), 258–261. CrossRefGoogle Scholar
  70. Walker, W. F. (1997). A computer participant in musical improvisation. In Proc. of ACM international conference on human factors in computing systems, Atlanta, Georgia (pp. 123–130). Google Scholar
  71. Weinberg, G., Godfrey, M., Rae, A., & Rhoads, J. (2008). A real-time genetic algorithm in human-robot musical improvisation. In R. Kronland-Martinet et al. (Eds.), LNCS: Vol. 4969. Proc. of 2007 computer music modeling and retrieval (pp. 351–359). CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Sony CSL-ParisParisFrance

Personalised recommendations