Appropriate and Complementary Rhythmic Improvisation in an Interactive Music System

Part of the Springer Series on Cultural Computing book series (SSCC)


One of the roles that interactive music systems can play is to operate as real-time improvisatory agents in an ensemble. A key issue for such systems is how to generate improvised material that is musically appropriate, and complementary to the rest of the ensemble. This chapter describes some improvisation strategies employed by the Jambot (a recently developed interactive music system) that combine both imitative and ‘intelligent’ techniques. The Jambot uses three approaches to mediate between imitative and intelligent actions: (i) mode switching based on confidence of understanding, (ii) filtering and elaboration of imitative actions, and (iii) measured deviation from imitative action according to a salient parametrisation of the action space. In order to produce appropriate rhythms the Jambot operates from a baseline of transformed imitation, and utilises moments of confident understanding to deviate musically from this baseline. The Jambot’s intelligent improvisation seeks to produce complementary rhythms by manipulating the level of ambiguity present in the improvisation to maintain a balance between novelty and coherence.


Music Perception Musical Experience Coherence Level Intelligent Action Musical Event 
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.


  1. Bharucha, J. (1991). Pitch, harmony and neural nets: A psychological perspective. In P. Todd & G. Loy (Eds.), Music and connectionism. Cambridge, MA: MIT Press.Google Scholar
  2. Borgo, D., & Goguen, J. (2004). Sync or swarm: Group dynamics in musical free improvisation. In R. Parncutt, A. Kessler, & F. Zimmer (Eds.), Conference of interdisciplinary musicology, University of Graz.Google Scholar
  3. Collins, N. (2006). Towards autonomous agents for live computer music: Real-time machine listening and interactive music systems. Dissertation, Cambridge University.Google Scholar
  4. Davis, M. (1999). The philosophy of poetry: On Aristotle’s poetics. South Bend: St. Augustine’s Press.Google Scholar
  5. Dean, R. (2003). Hyperimprovisation: Computer-interactive sound improvisation. Madison: A-R Editions.Google Scholar
  6. Gifford, T., & Brown, A. R. (2008). Stochastic onset detection: An approach to detecting percussive onsets attacks in complex audio. In Proceedings of the 2008 Australasian Computer Music Conference, Sydney. Melbourne: Australian Computer Music Association.Google Scholar
  7. Gifford, T., & Brown, A. R. (2009). Do androids dream of electric Chimaera? In Proceedings of the 2009 Australasian Computer Music Conference, Brisbane. Melbourne: Australian Computer Music Association.Google Scholar
  8. Gifford, T., & Brown, A. R. (2010). Anticipatory timing in algorithmic rhythm generation. In Proceedings of the 2010 Australasian Computer Music Conference, Canberra. Melbourne: Australian Computer Music Association.Google Scholar
  9. Huron, D. (2006). Sweet anticipation. Cambridge, MA: MIT Press.Google Scholar
  10. Jackendoff, R. (1992). Languages of the mind. Cambridge, MA: MIT Press.Google Scholar
  11. Jones, M. R. (1987). Dynamic pattern structure in music: Recent theory and research. Perception and Psychophysics, 41, 621–634.CrossRefGoogle Scholar
  12. Kivy, P. (2002). Introduction to the philosophy of music. Oxford: Oxford University Press.Google Scholar
  13. Large, E. W. (1994). Dynamic representation of musical structure. Dissertation, Ohio State University.Google Scholar
  14. Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. Cambridge, MA: MIT Press.Google Scholar
  15. London, J. (2004). Hearing in time. Oxford: Oxford University Press.CrossRefGoogle Scholar
  16. Machover, T., & Chung, J. (1989). Hyperinstruments: Musically intelligent and interactive performance and creativity systems. In Proceedings of the 15th international computer music conference, Columbus. San Francisco: International Computer Music Association.Google Scholar
  17. Meyer, L. (1956). Emotion and meaning in music. Chicago: Chicago University Press.Google Scholar
  18. Narmour, E. (1990). The analysis and cognition of basic musical structures. Chicago: University of Chicago Press.Google Scholar
  19. Pachet, F. (2006). Enhancing individual creativity with interactive musical reflective systems. In G. Wiggins & I. Deliege (Eds.), Musical creativity: Current research in theory and practice. London: Psychology Press.Google Scholar
  20. Pearce, M., & Wiggins, G. (2006). Expectation in melody: The influence of context and learning. Music Perception, 23(5), 377–405.CrossRefGoogle Scholar
  21. Persson, P., Laaksolahti, J., & Lonnqvist, P. (2001). Understanding socially intelligent agents: A multilayered phenomenon. IEEE Transactions on Systems, Man, and Cybernetics, 42(6), 349–360.Google Scholar
  22. Rowe, R. (1993). Interactive music systems. Cambridge, MA: MIT Press.Google Scholar
  23. Temperley, D. (2001). The cognition of basic musical structures. Cambridge, MA: MIT Press.Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Queensland Conservatorium of MusicGriffith UniversityBrisbaneAustralia

Personalised recommendations