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


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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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).

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