2003 | OriginalPaper | Buchkapitel
Evolutionary Music and the Zipf-Mandelbrot Law: Developing Fitness Functions for Pleasant Music
verfasst von : Bill Manaris, Dallas Vaughan, Christopher Wagner, Juan Romero, Robert B. Davis
Erschienen in: Applications of Evolutionary Computing
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.