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Automatic Synthesizer Preset Generation with PresetGen

Bibliographic Data

ID5305133
AuthorsKivanç Tatar (0000-0003-4133-8641, Simon Fraser University), Kıvanç Tatar (Simon Fraser University, Vancouver, Canada.), Matthieu Macret (Simon Fraser University, Vancouver, Canada.), Philippe Pasquier (0000-0001-8675-3561, Simon Fraser University, Vancouver, Canada., corresponding author)
Year2016
Volume45
Issue2
Pages124-144
Publication date2016-04-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of New Music Research (JOURNAL)
Journal identifiersISSN: 0929-8215 • E-ISSN: 1744-5027
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/09298215.2016.1175481
OpenAlexW2345690065
LanguageEN
Citations received1
References cited16

We refer the task of finding preset(s) (i.e. set(s) of synthesizer parameters) that approximates a target sound best, as the preset generation problem. PresetGen addresses this problem regarding the real world synthesizer, OP-1. The OP-1 consists of several synthesis blocks, and it is not fully deterministic. We propose and evaluate a solution to preset generation using a multi-objective Non-dominated Sorting-Genetic-Algorithm-II. PresetGen handles the full problem complexity and returns a small set of presets that approximate the target sound best by covering the Pareto front of this multi-objective optimization problem. Moreover, we present an empirical evaluation experiment that compares the performance of three human sound designers to that of PresetGen. The results show that PresetGen is human-competitive

Algorithm · Mathematical optimization · Programming language · Sorting · Computer Science · Engineering · Evolutionary Algorithms and Applications · Mathematics · Music and Audio Processing · Music Technology and Sound Studies

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Unique citing works1
Citations per year0.14
Citation span2019 - 2019 (1)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 1

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