Abstract
The Continuator system is an attempt to bridge the gap between two classes of traditionally incompatible musical systems: 1) interactive musical systems, limited in their ability to generate stylistically consistent material, and 2) music composition systems, which are fundamentally not interactive. The purpose of Continuator is to extend the technical ability of musicians with stylistically consistent, automatically learnt musical material. This requires the ability for the system to build operational representations of musical styles in real time, and to adapt quickly to external musical information. The Continuator is based on a Markov model of musical styles augmented to account for efficient real time learning of musical styles and to arbitrary external bias. The paper describes the main technical issues at stake concerning the integration of an agnostic learning scheme in an interactive instrument, and reports on realworld experiments performed with various musicians.
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References
Assayag, G. Dubnov, S. Delerue, O. Guessing the Composer’s Mind: Applying Universal Prediction to Musical Style, Proc. ICMC 99, Beijing, China, I.C.M.A., San-Francisco, 1999.
Baggi, D. L. NeurSwing: An Intelligent Workbench for the Investigation of Swing in Jazz, in Readings in Computer Generated Music, IEEE Computer Society Press, 1992.
Biles, John A. Interactive GenJam: Integrating Real-Time Performance with a Genetic Algorithm, Proc. ICMC 98, Ann Arbor, Michigan, 1998.
Jan Borchers, Designing Interactive Musical Systems: a Pattern Approach, HCI International’ 99. 8th International Conference on Human-Computer Interaction, Munich, Germany, from 22–27 August, 1999.
Conklin, D. and Witten, Ian H. Multiple Viewpoint Systems for Music Prediction, JNMR, 24:1, 51–73, 1995.
Cope, David. Experiments in Musical Intelligence. Madison, WI: A-R Editions, 1996.
Heuser, Jorg, Pat Martino—His contributions and influence to the history of modern Jazz guitar. Ph.D thesis, University of Mainz (Ge), 1994.
Hiller, L. and Isaacson, A., Experimental Music, New York: McGraw-Hill, 1959.
Karma music workstation, Basic guide. Korg Inc. Available at: http://www.korg.com/downloads/pdf/KARMA_BG.pdf, 2001.
Lartillot O., Dubnov S., Assayag G., Bejerano G., Automatic modeling of musical style Proc. ICMC 2001, La Habana, Cuba.
Orlarey, Y. Lequay, H. MidiShare: a Real Time multi-tasks software module for Midi applications Proceedings of the International Computer Music Conference, Computer Music Association, San Francisco, pp. 234–237, 1989.
Pachet, F. Roy, P. “Automatic Harmonization: a Survey”, Constraints Journal, Kluwer, 6:1, 2001.
Pachet, F. “Content-Based Management for Electronic Music Distribution”, Communications of the ACM, to appear, 2002.
Ramalho G., Ganascia J.-G. Simulating Creativity in Jazz Performance. Proceedings of the National Conference in Artificial Intelligence, pp. 108–113, AAAI-94, Seattle, AAAI Press, 1994.
Robert Rowe, Machine Musicianship, MIT Press, 2001.
Ron, D. Singer, Y and Tishby, N., “The power of amnesia: learning probabilistic automata with variable memory length”, Machine Learning 25(2–3):117–149, 1996.
J. L. Trivino-Rodriguez; R. Morales-Bueno, Using Multiattribute Prediction Suffix Graphs to Predict and Generate Music, Computer Music Journal 25(3) pp. 62–79, 2001.
William F. Walker A Computer Participant in Musical Improvisation, Proc. of CHI 1997. Atlanta, ACM Press, 1997.
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© 2002 Springer-Verlag Berlin Heidelberg
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Pachet, F. (2002). Interacting with a Musical Learning System: The Continuator. In: Anagnostopoulou, C., Ferrand, M., Smaill, A. (eds) Music and Artificial Intelligence. ICMAI 2002. Lecture Notes in Computer Science(), vol 2445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45722-4_12
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DOI: https://doi.org/10.1007/3-540-45722-4_12
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