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.
Papers & Posters:
Tatar, K., Macret, M., & Pasquier, P. (2016). Automatic Synthesizer Preset Generation with PresetGen. Journal of New Music Research, 1–21. http://doi.org/10.1080/09298215.2016.1175481
- 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 Automatic_Tuning_of_the_OP-1_Synthesizer_Using_a_Multi-objective_Genetic_Algorithm