Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation

• URL: http://arxiv.org/abs/2205.03043v1
• Date: Fri, 6 May 2022 06:55:29 GMT
• Title: Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation
• Authors: Zui Chen, Yansen Jing, Shengcheng Yuan, Yifei Xu, Jian Wu and Hang Zhao
• Abstract summary: The problem of estimating a set of parameters configuration that best restore a sound timbre is an important yet complicated problem. We proposed a multi-modal deep-learning-based pipeline Sound2 Synth, 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.
• Score: 19.13182347908491
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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