Evolutionary music and the zipf-mandelbrot law: developing fitness functions for pleasant music

Authors:
Bill Manaris;Dallas Vaughan;Christopher Wagner;Juan Romero;Robert B. Davis
Affiliations:
Computer Science Department, College of Charleston, Charleston, SC;Computer Science Department, College of Charleston, Charleston, SC;Computer Science Department, College of Charleston, Charleston, SC;RNASA Lab, Faculty of Computer Science, University of A Coruña, Spain;Department of Mathematics and Statistics, Miami University, Hamilton, OH
Venue:
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Year:
2003

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Abstract

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.