论文标题
Sound2Synth：通过FM合成器参数估算来解释声音
Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation
论文作者
论文摘要
合成器是一种电子乐器，现在已在现代音乐制作和声音设计中广泛使用。合成器的每个参数配置都会产生独特的音色，可以看作是独特的仪器。估计一组最能恢复声音音色的参数配置的问题是一个重要但复杂的问题，即：合成器参数估计问题。我们提出了一个基于多模式的深度学习管道Sound2syth，以及一个专门设计的网络结构Prime-Dination卷积（PDC）。我们的方法不仅实现了SOTA，而且还实现了对流行的FM合成器Dexed合成器的第一个现实世界中的第一个适用结果。
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.
