Abstract
A major challenge in computer-generated music is to produce music that sounds natural. This paper introduces NEAT Drummer, which takes steps toward natural creativity. NEAT Drummer evolves a kind of artificial neural network called a Compositional Pattern Producing Network (CPPN) with the NeuroEvolution of Augmenting Topologies (NEAT) method to produce drum patterns. An important motivation for this work is that instrument tracks can be generated as a function of other song parts, which, if written by humans, thereby provide a scaffold for the remaining auto-generated parts. Thus, NEAT Drummer is initialized with inputs from an existing MIDI song and through interactive evolution allows the user to evolve increasingly appealing rhythms for that song. This paper explains how NEAT Drummer processes MIDI inputs and outputs drum patterns. The net effect is that a compelling drum track can be automatically generated and evolved for any song.
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References
McCormack, J.: Open problems in evolutionary music and art. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 428–436. Springer, Heidelberg (2005)
London, J.: Rhythm (November 2007), http://www.grovemusic.com
Takagi, H.: Interactive evolutionary computation: Fusion of the capacities of EC optimization and human evaluation. Proceedings of the IEEE 89(9), 1275–1296 (2001)
Dawkins, R.: The Blind Watchmaker. Longman, Essex, U.K (1986)
Lindenmayer, A.: Mathematical models for cellular interaction in developement parts I and II. Journal of Theoretical Biology 18, 280–299 & 300–315 (1968)
Todd, S., Latham, W.: Evolutionary Art and Computers. Academic Press, London (1992)
Sims, K.: Artificial evolution for computer graphics. In: Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) 1991, pp. 319–328. ACM Press, New York (1991)
Nelson, G.L.: Sonomorphs: An application of genetic algorithms to growth and development of musical organisms. In: 4th Biennial Art and Technology Symp., pp. 155–169 (March 1993)
Husbands, P., Copely, P., Eldridge, A., Mandelis, J.: 1. Evolutionary Computer Music. Springer, London (2007)
Biles, J.A.: 2. Evolutionary Computer Music Springer. Springer, London (2007)
Todd, P.M., Loy, D.G.: Music and Connectionism. MIT Press, Cambridge (1991)
Chen, C.C.J., Miikkulainen, R.: Creating melodies with evolving recurrent neural networks. In: Proceedings of the 2001 International Joint Conference on Neural Networks (IJCNN-2001, Washington, DC), pp. 2241–2246. IEEE Press, Washington (2001)
Gomez, F., Miikkulainen, R.: Solving non-Markovian control tasks with neuroevolution. In: IJCAI-1999, KAUF-ADDR, KAUF, pp. 1356–1361 (1999)
Saravanan, N., Fogel, D.B.: Evolving neural control systems. IEEE Expert, 23–27 (June 1995)
Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10, 99–127 (2002)
Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification. JAIR 21, 63–100 (2004)
Stanley, K.O.: Compositional pattern producing networks: A novel abstraction of development. Genetic Programming and Evolvable Machines Special Issue on Developmental Systems 8(2), 131–162 (2007)
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Hoover, A.K., Rosario, M.P., Stanley, K.O. (2008). Scaffolding for Interactively Evolving Novel Drum Tracks for Existing Songs. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_44
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DOI: https://doi.org/10.1007/978-3-540-78761-7_44
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