PresetGen addresses the target preset generation problem – given a sound and a synthesizer, finding a preset that best approximate the target sound – in the case of 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. Our approach makes it possible to handle the problem complexity and returns a small set of presets that best approximate the target sound by covering the Pareto front of this multi-objective optimization problem. Moreover, we present an empirical evaluation experiment that compares performance of three human sound designers to that of PresetGen, and shows that PresetGen is human competitive.


Kıvanç Tatar, Matthieu Macret, , Philippe Pasquier.

Research paper and Posters

Tatar, K., Macret, M., & Pasquier, P. (2016). Automatic Synthesizer Preset Generation with PresetGen. Journal of New Music Research, 1–21.

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Macret, M. & Pasquier, P. (2013). “Automatic Tuning of the OP-1 Synthesizer Using a Multi-objective Genetic Algorithm” Proceedings of Sound and Music Computing Conference.

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