Lastly, a huge thank you to my collaborator Hao Hao (github.com/gudgud96) and supervisor Josh (www.eecs.qmul.ac.uk/~josh/) for their help and contributions!

Our code is open source (github.com/christhetree/mod_discovery) and the trained synths are available as VST plugins via the @neutone.bsky.social platform and SDK.
Listening samples, visualizations, plugins, and more can be found at christhetr.ee/mod_discovery (7/7)

We evaluate our modulation discovery framework on unseen real-world modulation curves, highly modulated synthetic and real-world audio, and on white-box, gray-box, and black-box synth architectures. (6/7)

We investigate three modulation signal parameterizations:
• Framewise (Frame)
• Low-pass filtered (LPF)
• Piecewise 2D Bézier curves (Spline)
We find that LPF and Spline yield human-readable curves that trade sound-matching accuracy for interpretability. (5/7)

We apply our approach to a differentiable synthesizer inspired by the popular soft synths Serum and Vital with wavetable, filter, and envelope modulations. We also demonstrate its ability to generalize to other DDSP synth architectures. (4/7)

We propose a self-supervised neural sound-matching approach that leverages modulation extraction, constrained control signal parameterizations, and differentiable digital signal processing (DDSP) to discover the modulations present in a sound. (3/7)

Modulations are a critical part of sound design, enabling the creation of complex, evolving audio. However, finding the modulations in a sound is difficult and typical sound-matching / parameter estimation systems don’t consider the structure or routing of underlying modulations. (2/7)

Modulation Discovery with Differentiable Digital Signal Processing

This week I’ll be at @waspaa.com presenting our work on discovering synthesizer modulation signals in arbitrary audio.

arXiv: arxiv.org/abs/2510.06204
web: christhetr.ee/mod_discovery
code: github.com/christhetree/mod_discovery