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Evolutionary computing in sound synthesis and interactive algorithmic composition

Brook, Martin Gregory

Evolutionary computing in sound synthesis and interactive algorithmic composition Thumbnail


Authors

Martin Gregory Brook



Contributors

Miroslav Spasov
Supervisor

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

Files

BrookMPhil2023 EvoComposer Demo Video (170.2 Mb)
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BrookMPhil2023 Demo Compositions (53.9 Mb)
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BrookMPhil2023 EvoComposer (Mac) 2 (181.7 Mb)
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BrookMPhil2023 EvoComposer_Windows (409.8 Mb)
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BrookMPhil2023 Mutation (VST for Windows) (52.5 Mb)
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