Abstract
PRISM is a probabilistic-logical programming language based on Prolog. We present a PRISM-implementation of a general model for polyphonic music, based on Hidden Markov Models. Its probability parameters are automatically learned by running the built-in EM-algorithm of PRISM on training examples. We show how the model can be used as a classifier for music that guesses the composer of unknown fragments of music. Then we use it to automatically compose new music.
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References
Bengio, Y.: Markovian models for sequential data. Neural Computing Surveys 2, 129–162 (1999)
Conklin, D.: Music Generation from Statistical Models. In: Proceedings of the AISB 2003 Symposium on Artificial Intelligence and Creativity in the Arts and Sciences, Aberystwyth, Wales, pp. 30–35 (2003)
Cope, D.: The Algorithmic Composer. A-R Editions, Madison (2000)
Henz, M., Lauer, S., Zimmermann, D.: COMPOzE—Intention-based Music Composition through Constraint Programming. In: ICTAI 1996: Proceedings of the 8th International Conference on Tools with Artificial Intelligence (ICTAI 1996), Washington, DC, USA, p. 118. IEEE Computer Society Press, Los Alamitos (1996)
Hudak, P., Makucevich, T., Gadde, S., Whong, B.: Haskore music notation - an algebra of music. Journal of Functional Programming 6(3), 465–483 (1996)
Marom, Y.: Improvising Jazz using Markov chains. Honours Thesis, University of Western Australia (1997)
Pollastri, E., Simoncelli, G.: Classification of Melodies by Composer with Hidden Markov Models. In: Proceedings of the First International Conference on WEB Delivering of Music, Firenze, Italy, pp. 88–95 (2001)
Rueda, C., Valencia, F.D.: Situation: A Constraint-Based Visual System for Musical Compositions. In: Agon, C. (ed.) The OM’s Composer Book, IRCAM Centre Pompidou, France (to appear)
Sato, T., Kameya, Y.: PRISM: A symbolic-statistical modeling language. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence, Nagoya, Japan, pp. 1330–1335 (1997)
Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research (JAIR) 15, 391–454 (2001)
Sneyers, J.: MIDI files of automatically generated music. Available at, http://www.cs.kuleuven.be/~jon/automatic_composition/
Truchet, C., Assayag, G., Codognet, P.: Visual and Adaptive Constraint Programming in Music. In: International Computer Music Conference (ICMC 2001), La Havana, Cuba (September 2001)
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Sneyers, J., Vennekens, J., De Schreye, D. (2005). Probabilistic-Logical Modeling of Music. In: Van Hentenryck, P. (eds) Practical Aspects of Declarative Languages. PADL 2006. Lecture Notes in Computer Science, vol 3819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11603023_5
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DOI: https://doi.org/10.1007/11603023_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30947-5
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