Skip to main content
Springer Nature Link
Log in

Evolutionary Computing for Expressive Music Performance

  • Chapter
The Art of Artificial Evolution

Part of the book series: Natural Computing Series ((NCS))

Summary

We describe an evolutionary approach to one of the most challenging problems in computer music: modeling the knowledge applied by a musician when performing a score of a piece in order to produce an expressive performance of the piece. We extract a set of acoustic features from jazz recordings, thereby providing a symbolic representation of the musician’s expressive performance. By applying an evolutionary algorithm to the symbolic representation, we obtain an interpretable expressive performance computational model. We use the model to generate automatically performances with the timing and energy expressiveness of a human saxophonist.

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

Access this chapter

Log in via an institution

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

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

Reference

  1. Horner, A., Goldberg, D. (1991). Genetic algorithms and computer-assisted music composition. In: Proceedings of the International Computer Music Conference

    Google Scholar 

  2. Dahlstedt, P., Nordhal, M.G. (2001). Living melodies: Coevolution of sonic communication. Leonardo Music Journal, 34(3): 243–248

    Article  Google Scholar 

  3. Tokui, N., Iba, H. (2000). Music composition with interactive evolutionary computation. In: Proceedings of the International Conference on Genetic Algorithms

    Google Scholar 

  4. Phon-Amnuaisuk, S., Wiggins, G.A. (1999). The four-part harmonisation problem: A comparison between genetic algorithms and a rule-based system. In: Proceedings of AISB’99. Edinburgh, Scotland

    Google Scholar 

  5. Biles, J.A. (1994). GenJam: A genetic algorithm for generating jazz solos. In: Proceedings of International Computer Music Conference

    Google Scholar 

  6. Grachten, M., Arcos, J.L., López de Mántaras, R. (2004). Evolutionary optimization of music performance optimization. In: Proceedings of Computer Music Modeling and Retrieval

    Google Scholar 

  7. Gabrielsson, A. (1999). The Performance of Music. Academic Press.

    Google Scholar 

  8. Michalski, R.S. (1969). On the quasi-minimal solution of the general covering problem. In: Proceedings of the First International Symposium on Information Processing

    Google Scholar 

  9. Holland, J.M. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press

    Google Scholar 

  10. Miranda, E.R. (2004). At the crossroads of evolutionary computation and music: Self-programming synthesizers, swarm orchestras and the origins of melody. Evolutionary Computation, 12(2)

    Google Scholar 

  11. Millen, D. (1990). Cellular automata music. In: Proceedings of the International Computer Music Conference

    Google Scholar 

  12. Hunt, A., Kirk, R., Orton, R. (1991). Musical applications of a cellular automata workstation. In: Proceedings of the International Computer Music Conference

    Google Scholar 

  13. Miranda, E.R. (1993). Cellular automata music: An interdisciplinary music project interface. Journal of New Music Research, 22(1)

    Google Scholar 

  14. Degazio, B. (1999). La evolucion de los organismos musicales. In Miranda, E.R., ed.: Musica y Nuevas Tecnologias: Perspectivas para el siglo XXI. ACC L’Angelot, 137–148

    Google Scholar 

  15. Waschka II, R. (1999). Avoiding the fitness bottleneck: Using genetic algorithms to compose orchestral music. In: Proceedings of the International Computer Music Conference. San Francisco, California. ICMA, 201–203

    Google Scholar 

  16. McAlpine, K., Miranda, E.R., Hogar, S. (1999). Composing music with algorithms: A case study system. Computer Music Journal, 23(2)

    Google Scholar 

  17. Manzolli, J., Moroni, A., von Zuben, F., Gudwin, R. (1999). An evolutionary approach applied to algorithmic composition. In: Proceedings of the Brazilian Symposium on Computer Music

    Google Scholar 

  18. Bilota, E., Pantano, P., Talarico, V. (2000). Synthetic harmonies: an approach to musical semiosis by means of cellular automata. In: Proceedings of Artificial Life

    Google Scholar 

  19. Mandelis, J. (2001). Genophone: An evolutionary approach to sound synthesis and performance. In: Proceedings of the International Workshop on Artificial Models for Musical Applications

    Google Scholar 

  20. Hazan, A., Ramirez, R., Maestre, E., Perez, A., Pertusa, A. (2006). Modelling expressive performance: A regression tree approach based on strongly typed genetic programming. In: Proceedings of the European Workshop on Evolutionary Music and Art. Budapest, Hungary

    Google Scholar 

  21. Madsen, S.T., Widmer, G. (2005). Exploring similarities in music performances with an evolutionary algorithm. In: Proceedings of the International FLAIRS Conference. AAAI Press

    Google Scholar 

  22. Ramirez, R., Hazan, A. (2005). Understanding expressive music performance using genetic algorithms. In: Proceedings of the European Workshop on Evolutionary Music and Art. Lauzane, Switzerland

    Google Scholar 

  23. Ramirez, R., Hazan, A., Gomez, E., Maestre, E. (2005). Understanding expressive transformations in saxophone jazz performances. Journal of New Music Research, 34-4: 319–330

    Article  Google Scholar 

  24. Ramirez, R., Hazan, A., Maestre, E., Serra, X. (2006). A Data Mining Approach to Expressive Music Performance Modeling. In: A Data Mining Approach to Expressive Music Performance Modeling. Springer

    Google Scholar 

  25. Lopez de Mantaras, R., Arcos, J.L. (2002). AI and music from composition to expressive performance. AI Magazine, 23(3)

    Google Scholar 

  26. Widmer, G. (2002). Machine discoveries: A few simple, robust local expression principles. Computer Music Journal

    Google Scholar 

  27. Widmer, G. (2002). In search of the horowitz factor: Interim report on a musical discovery project. In: Proceedings of the International Conference on Discovery Science

    Google Scholar 

  28. Tobudic, A., Widmer, G. (2003). Relational ibl in music with a new structural similarity measure. In: Proceedings of the International Conference on Inductive Logic Programming

    Google Scholar 

  29. Dovey, M.J. (1995). Analysis of rachmaninoff’s piano performances using inductive logic programming. In: Proceeding of the European Conference on Machine Learning. Springer

    Google Scholar 

  30. Van Baelen, E., De Raedt, L. (1996). Analysis and prediction of piano performances using inductive logic programming. In: Proceedings of International Conference in Inductive Logic Programming

    Google Scholar 

  31. Morales, E. (1997). PAL: A pattern-based first-order inductive system. Machine Learning, 26

    Google Scholar 

  32. Igarashi, S., Ozaki, T., Furukawa, K. (2002). Respiration reflecting musical expression: Analysis of respiration during musical performance by inductive logic programming. In: Proceedings of Second International Conference on Music and Artificial Intelligence

    Google Scholar 

  33. Johnson, M.L. (1992). An expert system for the articulation of Bach fugue melodies. In Baggi, D.L., ed.: Readings in Computer-Generated Music. IEEE Computer Society, 41–51

    Google Scholar 

  34. Bresin, R. (2000). Virtual Virtuosity: Studies in Automatic Music Performance. PhD thesis. Kungliga Tekniska Högskolan

    Google Scholar 

  35. Friberg, A. (1997). A Quantitative Rule System for Musical Performance. PhD thesis. Kungliga Tekniska Högskolan

    Google Scholar 

  36. Friberg, A., Bresin, R., Fryden, L. (2000). Music from motion: Sound level envelopes of tones expressing human locomotion. Journal of New Music Research, 29(3): 199–210

    Article  Google Scholar 

  37. Canazza, S., De Poli, G., Roda, A., Vidolin, A. (1997). Analysis and synthesis of expressive intention in a clarinet performance. In: Proceedings of the International Computer Music Conference

    Google Scholar 

  38. Klapuri, A. (1999). Sound onset detection by applying psychoacoustic knowledge. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing

    Google Scholar 

  39. Maher, R.C., Beauchamp, J.W. (1994). Fundamental frequency estimation of musical signals using a two-way mismatch procedure. Journal of the Acoustic Society of America, 95

    Google Scholar 

  40. Ramirez, R., Hazan, A. (2007). Inducing a generative expressive performance model using a sequential-covering genetic algorithm. In: GECCO 07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. New York, NY, USA. ACM Press, 2159–2166

    Google Scholar 

  41. Narmour, E. (1990). The Analysis and Cognition of Basic Melodic Structures: The Implication Realization Model. University of Chicago Press

    Google Scholar 

  42. Narmour, E. (1991). The Analysis and Cognition of Melodic Complexity: The Implication Realization Model. University of Chicago Press

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pompeu Fabra University, Music Technology Group, Ocata 1, 08003 Barcelona, Spain

    Rafael Ramirez, Amaury Hazan, Jordi Marine & Xavier Serra

Authors
  1. Rafael Ramirez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amaury Hazan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jordi Marine
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xavier Serra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information and Communication Technologies, University of A Coruña Campus de Elvina, 15071 La Coruña, Spain

    Juan Romero

  2. CISUC Dept. of Informatics Engineering, University of Coimbra, 3030 Coimbra, Portugal

    Penousal Machado

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ramirez, R., Hazan, A., Marine, J., Serra, X. (2008). Evolutionary Computing for Expressive Music Performance. In: Romero, J., Machado, P. (eds) The Art of Artificial Evolution. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72877-1_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72877-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72876-4

  • Online ISBN: 978-3-540-72877-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation