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Monterey Mirror: an experiment in interactive music performance combining evolutionary computation and Zipf’s law

Abstract

Monterey Mirror is an experiment in interactive music performance. It is engages a human (the performer) and a computer (the mirror) in a game of playing, listening, and exchanging musical ideas. The computer side involves an interactive stochastic music generator which incorporates Markov models, genetic algorithms, and power-law metrics. This approach combines the predictive power of Markov models with the innovative power of genetic algorithms, using power-law metrics for fitness evaluation. These power-law metrics have been developed and refined in a decade-long project, which explores music information retrieval based on Zipf’s law and related power laws. We describe the architecture of Monterey Mirror, which can generate musical responses based on aesthetic variations of user input. We also explore how such a system may be used as a musical meta-instrument/environment in avant-garde music composition and performance projects.

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References

  1. 1.

    Xenakis I (2001) Formalized Music: Thought and Mathematics in Music. Pendagon Press, Hillsdale

  2. 2.

    Cage J (1961) Silence: lectures and writings of John Case. Wesleyan University Press, Middletown

  3. 3.

    Rabiner LR, Juang BH (1986) An introduction to hidden Markov models. IEEE ASSP Mag 3(1):4–16

    Article  Google Scholar 

  4. 4.

    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA

  5. 5.

    Biles JA (2013) Straight-ahead jazz with GenJam: a quick demonstration. In: Proceedings of 2nd International Workshop on Musical Metacreation (MUME 2013), AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE’13), Boston, pp 67–74

  6. 6.

    Cope D (2001) Virtual music: computer synthesis of musical style. MIT Press, Cambridge

  7. 7.

    Pachet F (2002) Playing with virtual musicians: the continuator in practice. IEEE Multimed 9(3):77–82

    Article  Google Scholar 

  8. 8.

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

  9. 9.

    Wiggins G, Papapoulos G, Phon-Amnuaisuk S, Tuson A (1999) Evolutionary methods for musical composition. In: Dubois DM (ed) International journal of computer anticipatory systems, vol 4. CHAOS, pp.312–325

  10. 10.

    Brown AR (2002) Opportunities for evolutionary music composition. In: Proceedings Australasian Computer Music Conference, Melbourne, pp 27–34

  11. 11.

    McCormack J (1996) Grammar-based music composition. In: Stocker R, Jelinek H, Burnota B, Bossomaier T (eds) Complex systems 96: from local interactions to global phenomena. IOS Press, Amsterdam, pp 321–336

  12. 12.

    Burraston D, Edmonds E, Livingstone D, Miranda ER (2004) Cellular automata in MIDI based computer music. In: Proceedings of the International Computer Music Conference, pp 71–78

  13. 13.

    Manaris B, Romero J, Machado P, Krehbiel D, Hirzel T, Pharr W, Davis RB (2005) Zipf’s law, music, classification and aesthetics. Comput Music J 29(1):55–69

    Article  Google Scholar 

  14. 14.

    Manaris B, Armstrong JR, Zalonis T, Krehbiel D (2010) Armonique: a framework for web audio archiving, searching, and metadata extraction. Int Assoc Sound Audiov Arch J 35:57–68

    Google Scholar 

  15. 15.

    Manaris B, Machado P, McCauley C, Romero J, Krehbiel D (2005) Developing fitness functions for pleasant music: Zipf’s law and interactive evolution system. In: EvoMUSART2005—3rd European Workshop on Evolutionary Music and Art, Lausanne, Switzerland, pp 298–507

  16. 16.

    Zipf GK (1949) Human behavior and the principle of least effort. Hafner Publishing Company, New York

  17. 17.

    Voss RF, Clarke J (1975) ‘1/f noise’ in music and speech. Nature 258(5533):317–318

    Article  Google Scholar 

  18. 18.

    Schroeder M (1991) Fractals, chaos, power laws: minutes from an infinite paradise. W.H. Freeman, New York

  19. 19.

    Knich D, Real, artificial brains make magical music. In: The post and courier. http://tinyurl.com/musical-intelligence. Accessed 01 Nov 2013

  20. 20.

    Jurafsky D, Martin JH (2008) Speech and language processing: an introduction to natural language processing, computational linguistics and speech recognition. Prentice Hall, Upper Saddle River

  21. 21.

    Hsu KJ, Hsu AJ (1990) Fractal geometry of music. Proc Natl Acad Sci 87(3):938–941

    Article  Google Scholar 

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Acknowledgments

This work has been supported in part by National Science Foundation (Grants IIS-0736480, IIS-0849499 and IIS-1049554). The first author would like to acknowledge the contribution of David Cope, Peter Elsie, Daniel Brown, and Michael O’Bannon in shaping the Monterey Mirror concept during the 2010 Workshop in Algorithmic Computer Music (WACM) at the University of California, Santa Cruz. We also would like to acknowledge the following for their contributions in formulating and evaluating power-law metrics: Thomas Zalonis, Brys Sepulveda, Patrick Roos, Dwight Krehbiel, Luca Pellicoro, Timothy Hirzel, Penousal Machado, and Juan Romero. The authors would like to thank the reviewers for their valuable comments and suggestions to improve the quality of the paper.

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Correspondence to Bill Manaris.

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Manaris, B., Hughes, D. & Vassilandonakis, Y. Monterey Mirror: an experiment in interactive music performance combining evolutionary computation and Zipf’s law. Evol. Intel. 8, 23–35 (2015). https://doi.org/10.1007/s12065-014-0118-2

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Keywords

  • Genetic algorithms
  • Markov models
  • Zipf’s law
  • Power laws
  • Stochastic music
  • Music performance
  • Music composition