Since early 2022, Neutone FX has made the latest realtime neural audio models accessible to artists around the world. It includes a model browser that allows one to search for and download user models that have been shared and uploaded to the Neutone servers via the SDK. (6/8)

By encapsulating common challenges like variable buffer sizes, sample rate conversion, and delay compensation within a model-agnostic interface, our framework enables seamless interoperability between neural models and host plugins while allowing users to work entirely
in Python. (4/8)

The Neutone SDK is an open source framework that streamlines the deployment of PyTorch neural audio models for both real-time and offline applications. It enables researchers to wrap their own PyTorch models and run them in the DAW using our free host plugins FX and Gen. (3/8)

We’re also releasing the beta version of Neutone Gen, the counterpart to Neutone FX that continues to bridge the gap between audio researchers and artists. Now, you can export heavy-weight, non-realtime models using the SDK and run them in the DAW via the free Gen plugin. (2/8)

Neutone SDK: An Open Source Framework for Neural Audio Processing

We’ve finally published a paper for the Neutone SDK which I presented at AES AIMLA 2025 a few weeks ago!

arXiv: arxiv.org/abs/2508.09126
code: github.com/Neutone/neutone_sdk
discord: discord.gg/VHSMzb8Wqp

@neutone.bsky.social

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)

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