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

Abstract. A study on a 220-piece corpus (baroque, classical, romantic, 12tone, 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 musictheoretic 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.

• Type: Conference
• Year: 2003
• Where: EVOW
• Authors: Bill Z. Manaris, Dallas Vaughan, Christopher Wagner, Juan Romero, Robert B. Davis
• Keywords: Artificial Intelligence, EVOW 2003, Genre And/or Composer, Various Zipf-based Metrics, Zipf-based Metrics