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Interactive Evolution of 8–Bit Melodies with Genetic Programming towards Finding Aesthetic Measures for Sound

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7247)

Abstract

The efficient specification of aesthetic measures for music as a part of modelling human conception of sound is a challenging task and has motivated several research works. It is not only targeted to the creation of automatic music composers and raters, but also reinforces the research for a deeper understanding of human noesis. The aim of this work is twofold: first, it proposes an Interactive Evolution system that uses Genetic Programming to evolve simple 8–bit melodies. The results obtained by subjective tests indicate that evolution is driven towards more user–preferable sounds. In turn, by monitoring features of the melodies in different evolution stages, indications are provided that some sound features may subsume information about aesthetic criteria. The results are promising and signify that further study of aesthetic preference through Interactive Evolution may accelerate the progress towards defining aesthetic measures for sound and music.

Keywords

  • Genetic Program
  • Pulse Code Modulation
  • Interactive Evolution
  • Aesthetic Criterion
  • Symbolic Music

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Angeline, P.J.: Subtree crossover: Building block engine or macromutation? In: Proceedings of the Second Annual Conference on Genetic Programming 1997, pp. 9–17. Morgan Kaufmann (1997)

    Google Scholar 

  2. Burton, A.R., Vladimirova, T.: Generation of musical sequences with genetic techniques. Computer Music Journal 23(4), 59–73 (1999)

    CrossRef  Google Scholar 

  3. Ekárt, A., Sharma, D., Chalakov, S.: Modelling Human Preference in Evolutionary Art. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Drechsler, R., Farooq, M., Grahl, J., Greenfield, G., Prins, C., Romero, J., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Urquhart, N., Uyar, A.Ş. (eds.) EvoApplications 2011, Part II. LNCS, vol. 6625, pp. 303–312. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  4. Garcia, R.A.: Towards the automatic generation of sound synthesis techniques: Preparatory steps. In: Audio Engineering Society Convention 109 (September 2000)

    Google Scholar 

  5. Garcia, R.A.: Growing sound synthesizers using evolutionary methods. Synthesis M(Garcia 2000), 99–107 (2001)

    Google Scholar 

  6. Heikkilä, V.: Countercomplex–bitwise creations in a pre-apocalyptic world (December 2011), http://countercomplex.blogspot.com/

  7. Heikkilä, V.: Discovering novel computer music techniques by exploring the space of short computer programs. arXiv:1112.1368 (December 2011)

    Google Scholar 

  8. Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Phys. D 31, 277–283 (1988)

    MathSciNet  MATH  CrossRef  Google Scholar 

  9. Johanson, B.E., Poli, R.: GP-Music: an interactive genetic programming system for music generation with automated fitness raters. Tech. Rep. CSRP-98-13, University of Birmingham, School of Computer Science (May 1998)

    Google Scholar 

  10. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, 1st edn. The MIT Press (December 1992)

    Google Scholar 

  11. Manaris, B., Purewal, T., McCormick, C.: Progress towards recognizing and classifying beautiful music with computers - MIDI-encoded music and the Zipf-Mandelbrot law. In: Proceedings IEEE SoutheastCon, pp. 52–57. IEEE (2002)

    Google Scholar 

  12. Manaris, B., Romero, J., Machado, P., Krehbiel, D., Hirzel, T., Pharr, W., Davis, R.B.: Zipf’s law, music classification, and aesthetics. Computer Music Journal 29(1), 55–69 (2005); ArticleType: research-article / Full publication date: Spring, 2005 / Copyright 2005 The MIT Press

    CrossRef  Google Scholar 

  13. Manaris, B., Roos, P., Machado, P., Krehbiel, D., Pellicoro, L., Romero, J.: A corpus-based hybrid approach to music analysis and composition. In: Proceedings of the 22nd National Conference on Artificial Intelligence, vol. 1, pp. 839–845. AAAI Press (2007)

    Google Scholar 

  14. Phon-Amnuaisuk, S., Law, E., Kuan, H.: Evolving Music Generation with SOM-Fitness Genetic Programming. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 557–566. Springer, Heidelberg (2007)

    Google Scholar 

  15. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd (March 2008)

    Google Scholar 

  16. Putnam, J.B.: Genetic programming of music (August 30, 1994)

    Google Scholar 

  17. Roads, C.: Microsound. MIT Press (2004)

    Google Scholar 

  18. Sample of melodies–individuals created during a trial by a participant (2012), https://sites.google.com/site/maximoskp/SampleMelodies.zip

  19. Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5, 3–55 (2001)

    MathSciNet  CrossRef  Google Scholar 

  20. Silva, S., Almeida, J.: Gplab-a genetic programming toolbox for matlab. In: Proceedings of the Nordic MATLAB Conference, pp. 273–278 (2003)

    Google Scholar 

  21. Spector, L., Alpern, A.: Induction and recapitulation of deep musical structure. In: Proceedings of the IFCAI 1995 Workshop on Artificial Intelligence and Music, pp. 41–48 (1995)

    Google Scholar 

  22. Spector, L., Alpern, A.: Criticism, culture, and the automatic generation of artworks. In: Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI 1994, vol. 1, pp. 3–8. American Association for Artificial Intelligence, Menlo Park (1994)

    Google Scholar 

  23. Tokui, N.: Music composition with interactive evolutionary computation. Communication 17(2), 215–226 (2000)

    Google Scholar 

  24. Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5), 293–302 (2002)

    CrossRef  Google Scholar 

  25. Yan, J.R., Min, Y.: User fatigue in interactive evolutionary computation. Applied Mechanics and Materials 48-49, 1333–1336 (2011)

    CrossRef  Google Scholar 

  26. Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Transactions on Information Theory 23(3), 337–343 (1977)

    MathSciNet  MATH  CrossRef  Google Scholar 

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Kaliakatsos–Papakostas, M.A., Epitropakis, M.G., Floros, A., Vrahatis, M.N. (2012). Interactive Evolution of 8–Bit Melodies with Genetic Programming towards Finding Aesthetic Measures for Sound. In: Machado, P., Romero, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2012. Lecture Notes in Computer Science, vol 7247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29142-5_13

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  • DOI: https://doi.org/10.1007/978-3-642-29142-5_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29141-8

  • Online ISBN: 978-3-642-29142-5

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