SANTIAGO - A Real-Time Biological Neural Network Environment for Generative Music Creation

  • Hernán Kerlleñevich
  • Pablo Ernesto Riera
  • Manuel Camilo Eguia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6625)

Abstract

In this work we present a novel approach for interactive music generation based on the dynamics of biological neural networks. We develop SANTIAGO, a real-time environment built in Pd-Gem, which allows to assemble networks of realistic neuron models and map the activity of individual neurons to sound events (notes) and to modulations of the sound event parameters (duration, pitch, intensity, spectral content). The rich behavior exhibited by this type of networks gives rise to complex rhythmic patterns, melodies and textures that are neither too random nor too uniform, and that can be modified by the user in an interactive way.

Keywords

generative music biological neural networks real-time processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Danks, M.: Real-time image and video processing in Gem. In: Proceedings of the International Computer Music Conference, pp. 220–223. International Computer Music Association, San Francisco (1997)Google Scholar
  2. 2.
    Destexhe, A., Marder, E.: Plasticity in single neuron and circuit computations. Nature 431, 789–795 (2004)CrossRefGoogle Scholar
  3. 3.
    Eldridge, A.C.: Neural Oscillator Synthesis: generating adaptative signals with a continuous-time nerual modelGoogle Scholar
  4. 4.
    Franklin, J.A.: Recurrent neural networks for music computation. INFORMS Journal on Computing 18(3), 312 (2006)CrossRefGoogle Scholar
  5. 5.
    Galanter, P.: What is generative art? Complexity theory as a context for art theory. In: GA2003–6th Generative Art Conference (2003)Google Scholar
  6. 6.
    Gribbin, J.: Deep Simplicity: Bringing Order to Chaos and Complexity. Random House (2005)Google Scholar
  7. 7.
    Hooper, S.L.: Central Pattern Generators. Embryonic ELS (1999)Google Scholar
  8. 8.
    Huron, D.: Sweet anticipation: Music and the psychology of expectation. MIT Press, Cambridge (2008)Google Scholar
  9. 9.
    Izhikevich, E.M.: Simple model of spiking neurons. IEEE Transactions on Neural Networks 14(6), 1569–1572 (2004)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Izhikevich, E.M.: Dynamical systems in neuroscience: The geometry of excitability and bursting. The MIT press, Cambridge (2007)Google Scholar
  11. 11.
    Kocho, K., Segev, I.: The role of single neurons in information processing. Nature Neuroscience 3, 1171–1177 (2000)CrossRefGoogle Scholar
  12. 12.
    Matsuoka, K.: Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics 52(6), 367–376 (1985)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Molnár, G., et al.: Complex Events Initiated by Individual Spikes in the Human Cerebral Cortex. PLoS Biol. 6(9), e222 (2008)CrossRefGoogle Scholar
  14. 14.
    Mozer, M.C.: Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing. Connection Science 6(2), 247–280 (1994)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Puckette, M.S.: Pure Data. In: Proceedings, International Computer Music Conference, pp. 269–272. International Computer Music Association, San Francisco (1996)Google Scholar
  16. 16.
    Soltau, H., Schultz, T., Westphal, M., Waibel, A.: Recognition of music types. In: Proceedings of the 1998 IEEE International, vol. 2, pp. 1137–1140. IEEE, Los Alamitos (2002)Google Scholar
  17. 17.
    Taylor, I., Greenhough, M.: Modelling pitch perception with adaptive resonance theory artificial neural networks. Connection Science 6(6), 135–154 (1994)CrossRefGoogle Scholar
  18. 18.
    Yadegari, S.: Chaotic Signal Synthesis with Real-Time Control - Solving Differential Equations in Pd, Max-MSP, and JMax. In: Proceedings of the 6th International Conference on Digital Audio Effects (DAFx 2003), London (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hernán Kerlleñevich
    • 1
  • Pablo Ernesto Riera
    • 1
  • Manuel Camilo Eguia
    • 1
  1. 1.Laboratorio de Acústica y Percepción SonoraUniversidad Nacional de QuilmesBuenos AiresArgentina

Personalised recommendations