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Support for Learning Synthesiser Programming

Dykiert, M; Gold, NE; (2011) Support for Learning Synthesiser Programming. In: Proceedings of the 8th Sound and Music Computing Conference (SMC 2011). Padova University Press: Padova, Italy. Green open access

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When learning an instrument, students often like to emulate the sound and style of their favourite performers. The learning process takes many years of study and practice. In the case of synthesisers the vast parameter space involved can be daunting and unintuitive to the novice making it hard to define their desired sound and difficult to understand how it was achieved. Previous research has produced methods for automatically determining an appropriate parameter set to produce a desired sound but this can still require many parameters and does not explain or demonstrate the effect of particular parameters on the resulting sound. As a first step to solving this problem, this paper presents a new approach to searching the synthesiser parameter space to find a sound, reformulating it as a multi-objective optimisation problem (MOOP) where two competing objectives (closeness of perceived sonic match and number of parameters) are considered. As a proof-of-concept a pareto-optimal search algorithm (NSGA-II) is applied to CSound patches of varying complexity to generate a pareto-front of non-dominating (i.e. ”equally good”) solutions. The results offer insight into the extent to which the size and nature of parameter sets can be reduced whilst still retaining an acceptable degree of perceived sonic match between target and candidate sound.

Type: Proceedings paper
Title: Support for Learning Synthesiser Programming
Event: SMC 2011, 8th Sound and Music Computing Conference, 6-9 July 2011, Padova, Italy
Location: Padova, Italy
Dates: 06 July 2011 - 09 July 2011
ISBN-13: 9788897385035
Open access status: An open access version is available from UCL Discovery
Publisher version: http://smcnetwork.org/smc_papers-2011150
Language: English
Additional information: Copyright © 2011 Mateusz Dykiert et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1366745
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