Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation
This solves the complex problem of estimating synthesizer parameters from sound for music production and sound design, representing a strong specific gain rather than incremental.
The paper tackled the synthesizer parameters estimation problem by proposing Sound2Synth, a multi-modal deep-learning pipeline with a Prime-Dilated Convolution network, achieving state-of-the-art and the first real-world applicable results on the Dexed FM synthesizer.
Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design. Each parameters configuration of a synthesizer produces a unique timbre and can be viewed as a unique instrument. The problem of estimating a set of parameters configuration that best restore a sound timbre is an important yet complicated problem, i.e.: the synthesizer parameters estimation problem. We proposed a multi-modal deep-learning-based pipeline Sound2Synth, together with a network structure Prime-Dilated Convolution (PDC) specially designed to solve this problem. Our method achieved not only SOTA but also the first real-world applicable results on Dexed synthesizer, a popular FM synthesizer.