Musical Virtuosity and Creativity - Computers and Creativity - page 141

More generally, this chapter is an invitation to elevate virtuosity to a field of study
for cognitive science and computer science. Its links to creativity have only been
sketched here, but they are undoubtedly deeper and yet, unexplored. Understanding
virtuosity is a key to understanding creativity, in humans and with machines.

References

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.
Aebersold, J. (2000). The jazz handbook . Aebersold Jazz Inc. http://www.violistaz.com/

wp-content/uploads/2009/01/ebook-guitar-the-jazz-handbook.pdf .
Assayag, G., & Dubnov, S. (2004). Using factor oracles for machine improvisation. Soft Comput-
ing , 8 (9).
Bäckman, K., & Dahlstedt, P. (2008). A generative representation for the evolution of jazz solos.
In EvoWorkshops 2008 (Vol. 4974, pp. 371-380). Napoli: Springer.
Baggi, D. (2001). Capire il jazz, le strutture dello swing . Istituto CIM della Svizzera Italiana.
Baker, D. (2000). Bebop characteristics . Aebersold Jazz Inc.
Bensch, S., & Hasselquist, D. (1991). Evidence for active female choice in a polygynous warbler.
Animal Behavior , 44 , 301-311.
Biles, J. (1994). Genjam: a genetic algorithm for generating jazz solos. In Proc. of ICMC , Aarhus,
Denmark, ICMA.
Bresin, R. (2000). Virtual virtuosity, studies in automatic music performance .PhDthesis,KTH,
Stockholm, Sweden.
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).
Cappellini, G., Ivanenko, Y. P., Poppele, R. E., & Lacquaniti, F. (2006). Motor patterns in human
walking and running. Journal of Neurophysiology , 95 , 3426-3437.
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).
Coker, J. (1984). Jazz keyboard for pianists and non-pianists . Van Nuys: Alfred Publishing.
Coker, J. (1997). Complete method for improvisation (revised ed.). Van Nuys: Alfred Publishing.
Conklin, D. (2003). Music generation from statistical models. In Proceedings of symposium on AI
and creativity in the arts and sciences (pp. 30-35).
Conklin, D., & Witten, I. (1995). Multiple viewpoint systems for music prediction. Journal of New
Music Research , 24 , 51-73.
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.
Cope, D. (1996). Experiments in musical intelligence . Madison: A-R Editions.
Draganoiu, T. I., Nagle, L., & Kreutzer, M. (2002). Directional female preference for an exagger-
ated male trait in canary (serinus canaria) song. Proceedings of the Royal Society of London B ,
269 , 2525-2531.
Ericsson, K., Krampe, R., & Tesch-Römer, C. (1993). The role of deliberate practice in the acqui-
sition of expert performance. Psychological Review , 100 , 363-406.
Fitt, P. M. (1954). The information capacity of the human motor system in controlling the ampli-
tude of movement. Journal of Experimental Psychology , 47 (6), 381-391.
Franklin, J. A. (2006). Recurrent neural networks for music computation. INFORMS Journal on
Computing , 18 (3), 321-338.
Freuder, E. & Mackworth, A. (Eds.) (1994). Constraint-based reasoning . Cambridge: MIT Press.
Gladwell, M. (2008). Outliers, the story of success . London: Allen Lane.