Evolving the process of a virtual composer View Full Text


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Article Info

DATE

2019-03

AUTHORS

Csaba Sulyok, Andrew McPherson, Christopher Harte

ABSTRACT

In this paper we present a genetic programming system that evolves the music composition process rather than the musical product. We model the composition process using a Turing-complete virtual register machine, which renders musical pieces. These are evaluated using a series of fitness tests, which judge their statistical similarity against a corpus of Bach keyboard exercises. We explore the space of parameters for the system, looking specifically at population size, single-versus multi-track pieces and virtual machine instruction set design. Results demonstrate that the methodology succeeds in creating pieces of music that converge towards the properties of the chosen corpus. The output pieces exhibit certain musical qualities (repetition and variation) not specifically targeted by our fitness tests, emerging solely based on the statistical similarities. More... »

PAGES

47-60

References to SciGraph publications

  • 2009. Elevated Pitch: Automated Grammatical Evolution of Short Compositions in APPLICATIONS OF EVOLUTIONARY COMPUTING
  • 2009. The Evolution of Evolutionary Software: Intelligent Rhythm Generation in Kinetic Engine in APPLICATIONS OF EVOLUTIONARY COMPUTING
  • 2007. Composing with Genetic Algorithms: GenDash in EVOLUTIONARY COMPUTER MUSIC
  • 1999. Evolving Musical Harmonisation in ARTIFICIAL NEURAL NETS AND GENETIC ALGORITHMS
  • 2002-10-04. Opposites Attract: Complementary Phenotype Selection for Crossover in Genetic Programming in PARALLEL PROBLEM SOLVING FROM NATURE — PPSN VII
  • 2012-12. Corpus-based recombinant composition using a genetic algorithm in SOFT COMPUTING
  • 2011. Evolving Four-Part Harmony Using Genetic Algorithms in APPLICATIONS OF EVOLUTIONARY COMPUTATION
  • 2007. Evolutionary Computer Music in NONE
  • 2012-12. Musically meaningful fitness and mutation for autonomous evolution of rhythm accompaniment in SOFT COMPUTING
  • Journal

    TITLE

    Natural Computing

    ISSUE

    1

    VOLUME

    18

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11047-016-9561-6

    DOI

    http://dx.doi.org/10.1007/s11047-016-9561-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1007626434


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