DiffMoog: a Differentiable Modular Synthesizer for Sound Matching
Abstract
This paper presents DiffMoog - a differentiable modular synthesizer with a comprehensive set of modules typically found in commercial instruments. Being differentiable, it allows integration into neural networks, enabling automated sound matching, to replicate a given audio input. Notably, DiffMoog facilitates modulation capabilities (FM/AM), low-frequency oscillators (LFOs), filters, envelope shapers, and the ability for users to create custom signal chains. We introduce an open-source platform that comprises DiffMoog and an end-to-end sound matching framework. This framework utilizes a novel signal-chain loss and an encoder network that self-programs its outputs to predict DiffMoogs parameters based on the user-defined modular architecture. Moreover, we provide insights and lessons learned towards sound matching using differentiable synthesis. Combining robust sound capabilities with a holistic platform, DiffMoog stands as a premier asset for expediting research in audio synthesis and machine learning.
Cite
@article{arxiv.2401.12570,
title = {DiffMoog: a Differentiable Modular Synthesizer for Sound Matching},
author = {Noy Uzrad and Oren Barkan and Almog Elharar and Shlomi Shvartzman and Moshe Laufer and Lior Wolf and Noam Koenigstein},
journal= {arXiv preprint arXiv:2401.12570},
year = {2024}
}
Comments
5 pages, 7 figures, 1 table, Our code is released at https://github.com/aisynth/diffmoog