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

TL;DR

WildFX is a Docker-based pipeline that generates realistic multi-track audio effect datasets using a professional DAW, supporting complex plugin configurations and bridging AI modeling with real-world audio processing workflows.

Contribution

Introduces WildFX, a versatile, plugin-compatible pipeline for creating complex, multi-track audio effect datasets with a DAW backend, facilitating AI research in realistic audio DSP modeling.

Findings

Successfully generates multi-track datasets with rich effect graphs.

Demonstrates the pipeline's effectiveness in blind estimation of mixing parameters.

Bridges AI research with practical DSP workflows.

Abstract

Despite rapid progress in end-to-end AI music generation, AI-driven modeling of professional Digital Signal Processing (DSP) workflows remains challenging. In particular, while there is growing interest in neural black-box modeling of audio effect graphs (e.g. reverb, compression, equalization), AI-based approaches struggle to replicate the nuanced signal flow and parameter interactions used in professional workflows. Existing differentiable plugin approaches often diverge from real-world tools, exhibiting inferior performance relative to simplified neural controllers under equivalent computational constraints. We introduce WildFX, a pipeline containerized with Docker for generating multi-track audio mixing datasets with rich effect graphs, powered by a professional Digital Audio Workstation (DAW) backend. WildFX supports seamless integration of cross-platform commercial plugins or any plugins in the wild, in VST/VST3/LV2/CLAP formats, enabling structural complexity (e.g., sidechains, crossovers) and achieving efficient parallelized processing. A minimalist metadata interface simplifies project/plugin configuration. Experiments demonstrate the pipeline's validity through blind estimation of mixing graphs, plugin/gain parameters, and its ability to bridge AI research with practical DSP demands. The code is available on: https://github.com/IsaacYQH/WildFX.