Abstract
The creative potential of Genetic Algorithms (GAs) has been explored by many musicians who attempt to harness the unbound possibilities for creative search evident in nature. Within this paper, we investigate the possibility of using Continuous Time Recurrent Neural Networks (CTRNNs) as an evolvable low-level audio synthesis structure, affording users access to a vast creative search space of audio possibilities. Specifically, we explore some initial GA designs through the development of Plecto (see www.plecto.io), a creative tool that evolves CTRNNs for the discovery of audio. We have found that the evolution of CTRNNs offers some interesting prospects for audio exploration and present some design considerations for the implementation of such a system.
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Dahlstedt, P., Nordahl, M.G.: Living melodies: coevolution of sonic communication. Leonardo 34(3), 243–248 (2001)
McCormack, J.: Facing the future: evolutionary possibilities for human-machine creativity. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution, pp. 417–451. Springer, New York (2008)
McCormack, J.: Open problems in evolutionary music and art. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 428–436. Springer, Heidelberg (2005)
Bown, O.: Empirically grounding the evaluation of creative systems: incorporating interaction design. In: Proceedings of the Fifth International Conference on Computational Creativity (2014)
Husbands, P., Copley, P., Eldridge, A., Mandelis, J.: An introduction to evolutionary computing for musicians. In: Miranda, E.R., Biles, J.A. (eds.) Evolutionary Computer Music, pp. 1–27. Springer, New York (2007)
Tzimeas, D., Mangina, E.: Dynamic techniques for genetic algorithm-based music systems. Comput. Music J. 33(3), 45–60 (2009)
Bown, O.: Ecosystem models for real-time generative music: A methodology and framework. In: International Computer Music Conference (Gary Scavone 16 to 21 August 2009), The International Computer Music Association, pp. 537–540, August 2009
Tokui, N., Iba, H.: Music composition with interactive evolutionary computation. In: Proceedings of the 3rd International Conference on Generative Art, vol. 17, pp. 215–226 (2000)
Woolf, S., Yee-King, M.: Virtual and physical interfaces for collaborative evolution of sound. Contemp. Music Rev. 22(3), 31–41 (2003)
Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Campbell, A., Folsom-Kovarik, J.T., Stanley, K.O.: Picbreeder: a case study in collaborative evolutionary exploration of design space. Evol. Comput. 19(3), 373–403 (2011)
Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: ALIFE, pp. 329–336 (2008)
Yee-King, M., Roth, M.: Synthbot: an unsupervised software synthesizer programmer. In: Proceedings of the International Computer Music Conference, Ireland (2008)
Yee-King, M.J.: An automated music improviser using a genetic algorithm driven synthesis engine. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 567–576. Springer, Heidelberg (2007)
MacCallum, R.M., Mauch, M., Burt, A., Leroi, A.M.: Evolution of music by public choice. Proc. Nat. Acad. Sci. 109(30), 12081–12086 (2012)
Magnus, C., Cal IT CRCA: Evolving electroacoustic music: the application of genetic algorithms to time-domain waveforms. In: Proceedings of the 2004 International Computer Music Conference, pp. 173–176. Citeseer (2004)
Biles, J., Anderson, P., Loggi, L.: Neural network fitness functions for a musical IGA (1996)
Mozer, M.C.: Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing. Connection Sci. 6(2–3), 247–280 (1994)
Bown, O., Lexer, S.: Continuous-time recurrent neural networks for generative and interactive musical performance. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 652–663. Springer, Heidelberg (2006)
Ohya, K.: A sound synthesis by recurrent neural network. In: Proceedings of the 1995 International Computer Music Conference, pp. 420–423 (1995)
Eldridge, A.: Neural oscillator synthesis: Generating adaptive signals with a continuous-time neural model
Beer, R.D.: On the dynamics of small continuous-time recurrent neural networks. Adapt. Behav. 3(4), 469–509 (1995)
Blanco, A., Delgado, M., Pegalajar, M.: A genetic algorithm to obtain the optimal recurrent neural network. Int. J. Approximate Reasoning 23(1), 67–83 (2000)
Machado, P., Martins, T., Amaro, H., Abreu, P.H.: An interface for fitness function design. In: Romero, J., McDermott, J., Correia, J. (eds.) EvoMUSART 2014. LNCS, vol. 8601, pp. 13–25. Springer, Heidelberg (2014)
Jordà, S.: Faust music on line: An approach to real-time collective composition on the internet. Leonardo Music J. 9, 5–12 (1999)
McCormack, J.: Evolving sonic ecosystems. Kybernetes 32(1/2), 184–202 (2003)
Routen, T.: Techniques for the visualisation of genetic algorithms. In: Proceedings of the First IEEE Conference on Evolutionary Computation, 1994, IEEE World Congress on Computational Intelligence, pp. 846–851. IEEE (1994)
Mach, M.Z., Zetakova, M.: Visualising genetic algorithms: a way through the labyrinth of search space. In: Sincak, P., Vascak, J., Kvasnicak, V., Pospichal, J. (eds.) Intelligent Technologies-Theory and Applications, pp. 279–285. IOS Press, Amsterdam (2002)
Schedl, M., Höglinger, C., Knees, P.: Large-scale music exploration in hierarchically organized landscapes using prototypicality information. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, p. 8. ACM (2011)
Schwarz, D.: The sound space as musical instrument: playing corpus-based concatenative synthesis. New Interfaces for Musical Expression (NIME), pp. 250–253 (2012)
Plecto. http://www.plecto.io
Nelson, G.L.: Sonomorphs: An application of genetic algorithms to the growth and development of musical organisms. In: Proceedings of the Fourth Biennial Art & Technology Symposium, vol. 155 (1993)
Darwin Tunes. http://darwintunes.org
Picbreeder. http://www.picbreeder.org
Piamonte, D.P.T., Abeysekera, J.D., Ohlsson, K.: Understanding small graphical symbols: a cross-cultural study. Int. J. Ind. Ergon. 27(6), 399–404 (2001)
Dahlstedt, P.: Creating and exploring huge parameter spaces: Interactive evolution as a tool for sound generation. In: Proceedings of the 2001 International Computer Music Conference, pp. 235–242 (2001)
Gohlke, K., Hlatky, M., Heise, S., Black, D., Loviscach, J.: Track displays in daw software: beyond waveform views. In: Audio Engineering Society Convention 128, Audio Engineering Society (2010)
Sound Cloud. https://soundcloud.com
Sound Hunters. http://soundhunters.tv/create
Yu, G., Slotine, J.J.: Audio classification from time-frequency texture. arXiv preprint arxiv:0809.4501 (2008)
Google Maps. https://www.google.es/maps
Bown, O., McCormack, J.: Taming nature: tapping the creative potential of ecosystem models in the arts. Digital Creativity 21(4), 215–231 (2010)
Baluja, S., Pomerleau, D., Jochem, T.: Towards automated artificial evolution for computer-generated images. Connection Sci. 6(2–3), 325–354 (1994)
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Ianigro, S., Bown, O. (2016). Plecto: A Low-Level Interactive Genetic Algorithm for the Evolution of Audio. In: Johnson, C., Ciesielski, V., Correia, J., Machado, P. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2016. Lecture Notes in Computer Science(), vol 9596. Springer, Cham. https://doi.org/10.1007/978-3-319-31008-4_5
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