Evolutionary Music and the Zipf-Mandelbrot Law: Developing Fitness Functions for Pleasant Music

  • Bill Manaris
  • Dallas Vaughan
  • Christopher Wagner
  • Juan Romero
  • Robert B. Davis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)


A study on a 220-piece corpus (baroque, classical, romantic, 12-tone, jazz, rock, DNA strings, and random music) reveals that aesthetically pleasing music may be describable under the Zipf-Mandelbrot law. Various Zipf-based metrics have been developed and evaluated. Some focus on music-theoretic attributes such as pitch, pitch and duration, melodic intervals, and harmonic intervals. Others focus on higher-order attributes and fractal aspects of musical balance. Zipf distributions across certain dimensions appear to be a necessary, but not sufficient condition for pleasant music. Statistical analyses suggest that combinations of Zipf-based metrics might be used to identify genre and/or composer. This is supported by a preliminary experiment with a neural network classifier. We describe an evolutionary music framework under development, which utilizes Zipf-based metrics as fitness functions.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Bill Manaris
    • 1
  • Dallas Vaughan
    • 1
  • Christopher Wagner
    • 1
  • Juan Romero
    • 2
  • Robert B. Davis
    • 3
  1. 1.Computer Science DepartmentCollege of CharlestonCharlestonUSA
  2. 2.Creative Computer Group - RNASA Lab - Faculty of Computer ScienceUniversity of A CoruñaSpain
  3. 3.Department of Mathematics and StatisticsMiami UniversityHamiltonUSA

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