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Neural Proxies for Sound Synthesizers: Learning Perceptually Informed Preset Representations
Paolo Combes, Stefan Weinzierl, Klaus Obermayer
https://arxiv.org/abs/2509.07635 htt…

Neural Proxies for Sound Synthesizers: Learning Perceptually Informed Preset Representations
Deep learning appears as an appealing solution for Automatic Synthesizer Programming (ASP), which aims to assist musicians and sound designers in programming sound synthesizers. However, integrating software synthesizers into training pipelines is challenging due to their potential non-differentiability. This work tackles this challenge by introducing a method to approximate arbitrary synthesizers. Specifically, we train a neural network to map synthesizer presets onto an audio embedding space …