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A Viewpoint Approach to Symbolic Music Transformation

  • Louis Bigo
  • Darrell Conklin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9617)

Abstract

This paper presents a general approach to the transformation of symbolic music. The method is based on viewpoints, which enable the representation of musical surfaces by sequences of abstract features. Along the transformation process, some of these sequences are conserved while some others are variable and can be replaced by generated ones. The initial piece is therefore seen as a template which is instantiated at each transformation. The method is illustrated in the paper with the particular case of transformations occurring at the harmonic level. New chord sequences are generated by sampling from a statistical model in a particular style. The pitch of the notes constituting the template piece are then transformed according to the generated chord sequence.

Keywords

Harmonic transformation Viewpoints Computer-aided composition Harmonic analysis Music generation Statistical models Computational creativity 

Notes

Acknowledgments

The authors thank Dorien Herremans for valuable discussions and collaboration on this research. This research is supported by the project Lrn2Cre8 which is funded by the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET grant number 610859.

References

  1. 1.
  2. 2.
    Amatriain, X., Bonada, J., Loscos, A., Arcos, J.L., Verfaille, V.: Content-based transformations. J. New Music Res. 32(1), 95–114 (2003)CrossRefGoogle Scholar
  3. 3.
    Bellman, R.: Dynamic Programming, 1st edn. Princeton University Press, Princeton (1957)zbMATHGoogle Scholar
  4. 4.
    Bigo, L., Ghisi, D., Spicher, A., Andreatta, M.: Spatial transformations in simplicial chord spaces. In: Proceedings of the Joint International Computer Music Conference — Sound and Music Computing, Athens, pp. 1112–1119 (2014)Google Scholar
  5. 5.
    Chew, E.: The spiral array: an algorithm for determining key boundaries. In: Anagnostopoulou, C., Ferrand, M., Smaill, A. (eds.) ICMAI 2002. LNCS (LNAI), vol. 2445, pp. 18–31. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Conklin, D.: Music generation from statistical models. In: Proceedings of the AISB Symposium on Artificial Intelligence and Creativity in the Arts and Sciences, pp. 30–35. Aberystwyth, Wales (2003)Google Scholar
  7. 7.
    Conklin, D.: Discovery of distinctive patterns in music. Intell. Data Anal. 14(5), 547–554 (2010)Google Scholar
  8. 8.
    Conklin, D.: Multiple viewpoint systems for music classification. J. New Music Res. 42(1), 19–26 (2013)CrossRefGoogle Scholar
  9. 9.
    Conklin, D., Witten, I.: Multiple viewpoint systems for music prediction. J. New Music Res. 24(1), 51–73 (1995)CrossRefGoogle Scholar
  10. 10.
    Eigenfeldt, A., Pasquier, P.: Realtime generation of harmonic progressions using controlled Markov selection. In: Proceedings of ICCC-X-Computational Creativity Conference, pp. 16–25 (2010)Google Scholar
  11. 11.
    Fernandez, J.D., Vico, F.J.: AI methods in algorithmic composition: a comprehensive survey. J. Artif. Intell. Res. 48, 513–582 (2013)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Herremans, D., Sörensen, K., Conklin, D.: Sampling the extrema from statistical models of music with variable neighbourhood search. In: Proceedings of the Joint International Computer Music Conference — Sound and Music Computing, Athens, pp. 1096–1103 (2014)Google Scholar
  13. 13.
    Lerdahl, F.: Tonal Pitch Space. Oxford University Press, Oxford (2001)Google Scholar
  14. 14.
    Pachet, F., Roy, P.: Markov constraints: steerable generation of Markov sequences. Constraints 16(2), 148–172 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Pérez-Sancho, C., Rizo, D., Iñesta, J.M.: Genre classification using chords and stochastic language models. Connection Sci. 20(2&3), 145–159 (2009)CrossRefGoogle Scholar
  16. 16.
    Rocher, T., Robine, M., Hanna, P., Strandh, R.: Dynamic chord analysis for symbolic music. In: Proceedings of the International Computer Music Conference, Montreal, Quebec, Canada (2009)Google Scholar
  17. 17.
    Sleator, D., Temperley, D.: The Melisma music analyzer (2001). www.link.cs.cmu.edu/music-analysis

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of the Basque Country UPV/EHUSan SebastianSpain
  2. 2.IKERBASQUE, Basque Foundation for ScienceBilbaoSpain

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