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Assisted Lead Sheet Composition Using FlowComposer

  • Alexandre PapadopoulosEmail author
  • Pierre Roy
  • François Pachet
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9892)

Abstract

We present FlowComposer, a web application that helps users compose musical lead sheets, i.e. melodies with chord labels. FlowComposer integrates a constraint-based lead sheet generation tool in which the user retains full control over the generation process. Users specify the style of the lead sheet by selecting a corpus of existing lead sheets. The system then produces a complete lead sheet in that style, either from scratch, or from a partial lead sheet entered by the user. The generation algorithm is based on a graphical model that combines two Markov chains enriched by Regular constraints, representing the melody and its related chord sequence. The model is sampled using our recent result in efficient sampling of the Regular constraint. The paper reports on the design and deployment of FlowComposer as a web-service, part of an ecosystem of online tools for the creation of lead sheets. FlowComposer is currently used in professional musical productions, from which we collect and show a number of representative examples.

Keywords

Music generation Graphical models Belief propagation Sampling Web-service User interaction 
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Notes

Acknowledgment

This research is conducted within the Flow Machines project which received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 291156. We thank Benoît Carré and the team of the musical Beyond the Fence for their insightful comments in using the system. We thank Fiammetta Ghedini for creating the associated website with audio and video examples.

References

  1. 1.
    Anders, T., Miranda, E.R.: Constraint programming systems for modeling music theories and composition. ACM Comput. Surv. 43(4), 30:1–30:38 (2011)Google Scholar
  2. 2.
    Begleiter, R., El-Yaniv, R., Yona, G.: On prediction using variable order markov models. J. Artif. Intell. Res. (JAIR) 22, 385–421 (2004)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Boulanger-Lewandowski, N., Bengio, Y., Vincent, P.: Modeling temporal dependencies in high-dimensional sequences: application to polyphonic music generation and transcription, pp. 1159–1166 (2012)Google Scholar
  4. 4.
    Brooks, F.P., Hopkins, A., Neumann, P.G., Wright, W.: An experiment in musical composition. IRE Trans. Electron. Comput. 3, 175–182 (1957)CrossRefGoogle Scholar
  5. 5.
    Colton, S., Llano, M.T., Hepworth, R., Charnley, J., Gale, C.V., Baron, A., Pachet, F., Roy, P., Gervás, P., Collins, N., Sturm, B., Weyde, T., Wolff, D., Lloyd, J.: The Beyond the Fence musical and computer says show documentary. In: 7th International Conference on Computational Creativity (ICCC 2016), Paris, France, June 2016Google Scholar
  6. 6.
    Eck, D., Schmidhuber, J.: Learning the long-term structure of the blues. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 284–289. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Martín, D., Neullas, T., Pachet, F.: LeadsheetJS: a Javascript library for online lead sheet editing. In: 1st International Conference on Technologies for Music Notation and Representation (TENOR 2015), Paris, France, May 2015Google Scholar
  8. 8.
    Nierhaus, G.: Algorithmic Composition: Paradigms of Automated Music Generation. Springer, New York (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Pachet, F., Roy, P., Barbieri, G.: Finite-length Markov processes with constraints. In: IJCAI, pp. 635–642 (2011)Google Scholar
  10. 10.
    Pachet, F., Suzda, J., Martinez, D.: A comprehensive online database of machine-readable lead-sheets for jazz standards. In: de Souza Britto Jr., A., Gouyon, F., Dixon, S.(ed.) ISMIR, pp. 275–280 (2013)Google Scholar
  11. 11.
    Pachet, F.: Flow-Machines: CP techniques to model style in music and text. ACP (Association for Constraint Programming) (2015). http://www.a4cp.org/node/1066
  12. 12.
    Papadopoulos, A., Pachet, F., Roy, P., Sakellariou, J.: Exact sampling for regular and Markov constraints with belief propagation. In: Pesant, G. (ed.) CP 2015. LNCS, vol. 9255, pp. 341–350. Springer, Heidelberg (2015)Google Scholar
  13. 13.
    Pesant, G.: A regular language membership constraint for finite sequences of variables. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 482–495. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Rhodes, C., Lewis, D., Müllensiefen, D.: Bayesian model selection for harmonic labelling. In: Klouche, T., Noll, T. (eds.) MCM 2007. CCIS, vol. 37, pp. 107–116. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Roy, P., Pachet, F.: Enforcing meter in finite-length Markov sequences. In: des Jardins, M., Littman, M.L. (ed.) AAAI. AAAI Press (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alexandre Papadopoulos
    • 1
    • 2
    Email author
  • Pierre Roy
    • 1
  • François Pachet
    • 1
    • 2
  1. 1.Sony CSLParisFrance
  2. 2.Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6ParisFrance

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