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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
Destexhe, A., Marder, E.: Plasticity in single neuron and circuit computations. Nature 431, 789–795 (2004)
Eldridge, A.C.: Neural Oscillator Synthesis: generating adaptative signals with a continuous-time nerual model
Franklin, J.A.: Recurrent neural networks for music computation. INFORMS Journal on Computing 18(3), 312 (2006)
Galanter, P.: What is generative art? Complexity theory as a context for art theory. In: GA2003–6th Generative Art Conference (2003)
Gribbin, J.: Deep Simplicity: Bringing Order to Chaos and Complexity. Random House (2005)
Hooper, S.L.: Central Pattern Generators. Embryonic ELS (1999)
Huron, D.: Sweet anticipation: Music and the psychology of expectation. MIT Press, Cambridge (2008)
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Transactions on Neural Networks 14(6), 1569–1572 (2004)
Izhikevich, E.M.: Dynamical systems in neuroscience: The geometry of excitability and bursting. The MIT press, Cambridge (2007)
Kocho, K., Segev, I.: The role of single neurons in information processing. Nature Neuroscience 3, 1171–1177 (2000)
Matsuoka, K.: Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics 52(6), 367–376 (1985)
Molnár, G., et al.: Complex Events Initiated by Individual Spikes in the Human Cerebral Cortex. PLoS Biol. 6(9), e222 (2008)
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)
Puckette, M.S.: Pure Data. In: Proceedings, International Computer Music Conference, pp. 269–272. International Computer Music Association, San Francisco (1996)
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)
Taylor, I., Greenhough, M.: Modelling pitch perception with adaptive resonance theory artificial neural networks. Connection Science 6(6), 135–154 (1994)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kerlleñevich, H., Riera, P.E., Eguia, M.C. (2011). SANTIAGO - A Real-Time Biological Neural Network Environment for Generative Music Creation. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20520-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-20520-0_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20519-4
Online ISBN: 978-3-642-20520-0
eBook Packages: Computer ScienceComputer Science (R0)