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Title: Automatic sound synthesizer programming : techniques and applications
Author: Yee-King, Matthew John
ISNI:       0000 0004 2723 1810
Awarding Body: University of Sussex
Current Institution: University of Sussex
Date of Award: 2011
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The aim of this thesis is to investigate techniques for, and applications of automatic sound synthesizer programming. An automatic sound synthesizer programmer is a system which removes the requirement to explicitly specify parameter settings for a sound synthesis algorithm from the user. Two forms of these systems are discussed in this thesis: tone matching programmers and synthesis space explorers. A tone matching programmer takes at its input a sound synthesis algorithm and a desired target sound. At its output it produces a configuration for the sound synthesis algorithm which causes it to emit a similar sound to the target. The techniques for achieving this that are investigated are genetic algorithms, neural networks, hill climbers and data driven approaches. A synthesis space explorer provides a user with a representation of the space of possible sounds that a synthesizer can produce and allows them to interactively explore this space. The applications of automatic sound synthesizer programming that are investigated include studio tools, an autonomous musical agent and a self-reprogramming drum machine. The research employs several methodologies: the development of novel software frameworks and tools, the examination of existing software at the source code and performance levels and user trials of the tools and software. The main contributions made are: a method for visualisation of sound synthesis space and low dimensional control of sound synthesizers; a general purpose framework for the deployment and testing of sound synthesis and optimisation algorithms in the SuperCollider language sclang; a comparison of a variety of optimisation techniques for sound synthesizer programming; an analysis of sound synthesizer error surfaces; a general purpose sound synthesizer programmer compatible with industry standard tools; an automatic improviser which passes a loose equivalent of the Turing test for Jazz musicians, i.e. being half of a man-machine duet which was rated as one of the best sessions of 2009 on the BBC's 'Jazz on 3' programme.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: M1470 Aleatory music. Electronic music. Mixed media ; TK7800 Electronics