Creating a fitness function for music is largely subjective and dependent on a programmer's personal tastes or goals. Previous attempts to create musical fitness functions for use in genetic algorithms lack scope or are prejudiced to a certain genre of music. They also suffer the limitation of producing music only in the strict style determined by the programmer. We show in this paper that musical feature extractors that avoid the challenges of qualitative judgment enable creation of a multi-objective function for direct music production. Multi-objective fitness functions enable creation of music with varying identifiable styles. With this system we produced three distinct groups of music which computationally cluster into distinct styles as described by the set of feature extractors. We also show that knowledgeable individuals make similar clusters while a random sample of people make some similar and some different clusterings.
College and Department
Physical and Mathematical Sciences; Computer Science
BYU ScholarsArchive Citation
Murray, Skyler James, "Algorithmically Flexible Style Composition Through Multi-Objective Fitness Functions" (2012). Theses and Dissertations. 3382.
Music Composition, Genetic Algorithms, Feature Extractors