Synthesizers are widely used electronic musical instruments. Given an input sound, inferring the underlying synthesizer's parameters to reproduce it is a difficult task known as sound-matching. In this work, we tackle the problem of automatic sound matching, which is otherwise performed manually by professional audio experts. The novelty of our work stems from the introduction of a novel differentiable synthesizer-proxy that enables gradient-based optimization by comparing the input and reproduced audio signals. Additionally, we introduce a novel self-supervised finetuning mechanism that further refines the prediction at inference time. Both contributions lead to state-of-the-art results, outperforming previous methods across various metrics. Our code is available at: https://github.com/inversynth/InverSynth2.

Poster session Zoom meeting

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