Evolutionary computing in sound synthesis and interactive algorithmic composition

Brook, Martin Gregory

Abstract

Interactive genetic algorithms (IGAs) present an intriguing and productive way to explore problem space in creative applications and a variety of work has been carried out in this area. This thesis explores the potential for the application of IGAs to two aspects of music composition (sound synthesis and algorithmic composition) as well as the exploration and construction of the necessary parameter based domains/interfaces in which they are applied.

Modern parameter based software and hardware synthesisers can be technically complex and difficult to use often involving hundreds of parameters along with equally complex graphical user interfaces (GUI's). The application of IGAs to sound synthesis offers an alternative to the creation of sounds via the manual adjustment of individual controls/parameters which is both enjoyable and productive while also permitting rich and complex sound synthesis techniques involving many hundreds of parameters. 'Mutation', a C++ based software synthesiser allows for the application of IGAs to populations of sounds produced with a variety of experimental sound synthesis and processing techniques.

Algorithmic composition (the use of computational algorithms in the generation of music) may be an area of great future potential due to growth in computer performance and the rapid development of computer science in areas such as artificial intelligence. 'Evo-Composer' a compositional tool created in Max/MSP combines IGAs with an interactive rule based algorithmic composition system. This system allows for the evolution of material which may be played/modified intuitively via MIDI input.

Citation

Brook, M. G. Evolutionary computing in sound synthesis and interactive algorithmic composition. (Thesis). Keele University. https://keele-repository.worktribe.com/output/524017

• Thesis Type: Thesis
• Deposit Date: Jul 28, 2023
• Publicly Available Date: Jul 28, 2023
• Keywords: Music
• Public URL: https://keele-repository.worktribe.com/output/524017
• Award Date: 2023-06