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DiffMoog: a Differentiable Modular Synthesizer for Sound Matching

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arxiv 2401.12570 v1 pith:K3XFCB3Q submitted 2024-01-23 eess.AS cs.AIcs.SD

Noy Uzrad , Oren Barkan , Almog Elharar , Shlomi Shvartzman , Moshe Laufer , Lior Wolf , Noam Koenigstein This is my paper · ORCID
classification eess.AScs.AIcs.SD
keywords diffmoogsounddifferentiablematchingmodularaudiocapabilitiesframework
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DDSynth-RL: Audio Synthesizer Inversion via Discrete Diffusion with Reinforcement Learning

    cs.SD 2026-08 conditional novelty 6.0 of 10

    A masked discrete diffusion model fine-tuned with GRPO on rendered-audio rewards improves out-of-domain synthesizer parameter estimation on the Dexed FM synthesizer.

  2. WildFX: A DAW-Powered Pipeline for In-the-Wild Audio FX Graph Modeling

    cs.SD 2025-07 conditional novelty 6.0 of 10

    WildFX generates multi-track audio datasets by rendering real DAW effect graphs with commercial plugins inside Docker, and demonstrates the pipeline on blind mixing-graph estimation.

  3. Audio synthesizer inversion in symmetric parameter spaces with approximately equivariant flow matching

    cs.SD 2025-06 conditional novelty 6.0 of 10

    A flow-matching model equipped with a learned permutation-equivariant token mapping outperforms regression and generative baselines at inferring synthesizer parameters from audio.

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