Nothing Special   »   [go: up one dir, main page]

Skip to main content

Probabilistic-Logical Modeling of Music

  • Conference paper
Practical Aspects of Declarative Languages (PADL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 3819))

Included in the following conference series:

  • 331 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bengio, Y.: Markovian models for sequential data. Neural Computing Surveys 2, 129–162 (1999)

    Google Scholar 

  2. 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)

    Google Scholar 

  3. Cope, D.: The Algorithmic Composer. A-R Editions, Madison (2000)

    Google Scholar 

  4. 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)

    Chapter  Google Scholar 

  5. Hudak, P., Makucevich, T., Gadde, S., Whong, B.: Haskore music notation - an algebra of music. Journal of Functional Programming 6(3), 465–483 (1996)

    Article  Google Scholar 

  6. Marom, Y.: Improvising Jazz using Markov chains. Honours Thesis, University of Western Australia (1997)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research (JAIR) 15, 391–454 (2001)

    MATH  MathSciNet  Google Scholar 

  11. Sneyers, J.: MIDI files of automatically generated music. Available at, http://www.cs.kuleuven.be/~jon/automatic_composition/

  12. 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/11603023_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30947-5

  • Online ISBN: 978-3-540-31685-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics