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

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.


Fractal Dimension Interval Graph Relative Balance Composite Metrics Neural Network Classifier 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  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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