A probabilistic expert system for automatic musical accompaniment

Journal of Computational and Graphical Statistics

Volumn 10, Issue 3, 2001, Pages 487-512

  Raphael, Christopher ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

Bayesian belief networks; Conditional gaussian distribution; EM algorithm; Graphical model; Music

Indexed keywords

EID: 0035617328     PISSN: 10618600     EISSN: 15372715     Source Type: Journal    
DOI: 10.1198/106186001317115081     Document Type: Article

References (19)

1. Baird, B., Blevins, D., and Zahler, N. (1993), “Artificial Intelligence and Music: Implementing an Interactive Computer Performer,” Computer Music Journal, 17, 73-79.
2. Beran, J., and Mazzola, G. (1999), “Analyzing Musical Structure and Performance-A Statistical Approach,” Statistical Science, 14, 47-79.
3. Bloch, J., and Dannenberg, R. (1985), “Real-Time Computer Accompaniment of Keyboard Performances,” Pro ceedings of the International Computer Music Conference, 1985, Burnaby, British Columbia, Canada, pp. 279-289.
4. Bryson, J. (1992), “The Subsumption Strategy Development of a Music Modelling System,” unpublished masters’ thesis, Dept. of Artificial Intelligence, University of Edinburgh.
5. Bryson, J. (1995), “The Reactive Accompanist: Adaptation and Behaviour Decomposition in a Music System,” in The Biology and Technology of Intelligent Autonomous Agents, ed. L. Steels, London: Springer-Verlag.
6. Dannenberg, R. (1984), “An On-Line Algorithm for Real-Time Accompaniment,” in Proceedings of the Interna tional Computer Music Conference, 1984, Paris, France: IRCAM, pp. 193-198.
7. Dannenberg, R., and Mont-Reynaud, B. (1987), “Following an Improvisation in Real Time,” in Proceedings of the International Computer Music Conference, 1987, pp. 241-258.
8. Dannenberg, R., and Mukaino, H. (1988), “New Techniques for Enhanced Quality of Computer Accompaniment,” in Proceedings of the International Computer Music Conference, 1988, Koln, pp. 243-249.
9. Dempster A. P, Laird N. M., and Rubin D. B. (1977) “Maximum Likelihood from Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society, Series B, 39, 1-38.
10. Jensen, F. (1996), “An Introduction to Bayesian Networks,” New York: Springer-Verlag.
11. Jensen, F., Olesen, K., and Andersen, S. (1990), “An Algebra of Bayesian Belief Universes for Knowledge-Based Systems,” in Proceedings of the 8th International Joint Conference on Artificial Intelligence, American Association for Artificial Intelligence, pp. 190-193.
12. Kashino K., and Hagita, N. (1996), “A Music Scene Analysis System with the MRF-Based Information Integration Scheme,” in Proceedings of the International Conference on Pattern Recogntion, pp. 725-729.
13. Lauritzen, S. L. (1992), “Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models,” Journal of the American Statistical Association, 87, 1098-1108.
14. Lauritzen, S. L. (1995), “The EM Algorithm for Graphical Association Models with Missing Data,” Computational Statis tics and Data Analysis, 19, 191-201.
15. Lauritzen, S. L. (1996), “Graphical Models,” New York: Oxford University Press.
16. Raphael, C. (1999), “Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 360-370.
17. Spiegelhalter, D., Dawid, A. P, Lauritzen, S., and Cowell, R. (1993), “Bayesian Analysis in Expert Systems,” Statistical Science, 8, 219-283.
18. Vercoe, B. (1984), “The Synthetic Performer in the Context ofLive Performance,” in Proceedingsof the Interna tional Computer Music Conference, 1984 Paris, France: IRCAM, pp. 199-200.
19. Vercoe, B., and Puckette, M. (1985), “Synthetic Rehearsal: Training the Synthetic Performer,” in Proceedings of the International Computer Music Conference, 1985 Burnaby, British Columbia, Canada, pp. 275-278