Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation
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.
Cite
Text
Chen et al. "Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/682Markdown
[Chen et al. "Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/chen2022ijcai-sound/) doi:10.24963/IJCAI.2022/682BibTeX
@inproceedings{chen2022ijcai-sound,
title = {{Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation}},
author = {Chen, Zui and Jing, Yansen and Yuan, Shengcheng and Xu, Yifei and Wu, Jian and Zhao, Hang},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {4921-4928},
doi = {10.24963/IJCAI.2022/682},
url = {https://mlanthology.org/ijcai/2022/chen2022ijcai-sound/}
}