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arXiv · 2401.12570

DiffMoog: a Differentiable Modular Synthesizer for Sound Matching

Noy Uzrad·Oren Barkan·Almog Elharar·Shlomi Shvartzman·Moshe Laufer·Lior Wolf·Noam Koenigstein
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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.

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Noy Uzrad, Oren Barkan, Almog Elharar, Shlomi Shvartzman, Moshe Laufer, Lior Wolf, Noam Koenigstein. 2024-01-23. DiffMoog: a Differentiable Modular Synthesizer for Sound Matching. https://arxiv.org/abs/2401.12570

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