Compiling Differentiable Audio Graphs to Real-Time DSP

2026-06-19

AI Summary

This work addresses the error-prone and labor-intensive process of manually rewriting differentiable audio processors for real-time deployment, which often lacks formal verification. To bridge this gap, the authors propose ADAC, a compiler that automatically translates trained differentiable audio models into efficient FAUST code via a framework-agnostic intermediate representation, enabling end-to-end deployment. ADAC supports real-time hot-swapping, incorporates built-in stability verification, and facilitates macro-control parameter design, ensuring that the exported audio plugins match the original model’s impulse response within floating-point precision. Experimental results demonstrate that ADAC successfully converts feedback delay network models into fully functional, stable, and reliable real-time audio plugins, with discrepancies reduced to the level of floating-point noise, thereby establishing a robust pathway from research prototypes to production-grade applications.

Abstract

Differentiable audio processors are habitually designed and optimised in machine-learning frameworks, but deploying them as real-time audio effects still often requires non-automatic implementation in a dedicated digital signal processing language. The translation is error-prone, demands an onerous verification process, and detaches research prototypes from usable production tools. That being so, we present ADAC, a compiler that lowers a trained model to a framework-agnostic intermediate representation and emits efficient FAUST code whose impulse response matches the source model to within floating-point arithmetic noise, direct paths included. The optimisation loop is made audible by replacing the model in a running plugin after each gradient step. The exported processor carries a small set of macro-controls that leave its stability intact. A stability certificate computed from the shipped parameters is checked before the plugin is built. At the demonstration, a feedback delay network is trained and exported to a working plugin.

Problem

• differentiable audio processing
• real-time DSP
• audio plugin deployment
• model-to-code translation
• FAUST

Innovation

• differentiable audio processing
• compiler
• FAUST
• real-time DSP
• stability certification