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

arXiv: https://arxiv.org/abs/2507.10534 · HTML: https://mlsanity.com/paper/2507.10534/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 26 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity](https://mlsanity.com/paper/1909.08494/) (2019)
- [YuE: Scaling Open Foundation Models for Long-Form Music Generation](https://mlsanity.com/paper/2503.08638/) (2025)
- [SingSong: Generating musical accompaniments from singing](https://mlsanity.com/paper/2301.12662/) (2023)
- [Automatic multitrack mixing with a differentiable mixing console of neural audio effects](https://mlsanity.com/paper/2010.10291/) (2020)
- [Style Transfer of Audio Effects with Differentiable Signal Processing](https://mlsanity.com/paper/2207.08759/) (2022)
- [Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects](https://mlsanity.com/paper/2211.02247/) (2022)
- [Presto! Distilling Steps and Layers for Accelerating Music Generation](https://mlsanity.com/paper/2410.05167/) (2024)
- [Fast Text-to-Audio Generation with Adversarial Post-Training](https://mlsanity.com/paper/2505.08175/) (2025)
- [DiffMoog: a Differentiable Modular Synthesizer for Sound Matching](https://mlsanity.com/paper/2401.12570/) (2024)
- [ST-ITO: Controlling Audio Effects for Style Transfer with Inference-Time Optimization](https://mlsanity.com/paper/2410.21233/) (2024)
- [Modulation Extraction for LFO-driven Audio Effects](https://mlsanity.com/paper/2305.13262/) (2023)
- [Differentiable Black-box and Gray-box Modeling of Nonlinear Audio Effects](https://mlsanity.com/paper/2502.14405/) (2025)
- [Blind Estimation of Audio Processing Graph](https://mlsanity.com/paper/2303.08610/) (2023)
- [SignalTrain: Profiling Audio Compressors with Deep Neural Networks](https://mlsanity.com/paper/1905.11928/) (2019)
- [Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/2012.03216/) (2020)
- [InstrumentGen: Generating Sample-Based Musical Instruments From Text](https://mlsanity.com/paper/2311.04339/) (2023)
- [Singing Voice Synthesis Using Differentiable LPC and Glottal-Flow-Inspired Wavetables](https://mlsanity.com/paper/2306.17252/) (2023)
- [High-Fidelity Noise Reduction with Differentiable Signal Processing](https://mlsanity.com/paper/2310.11364/) (2023)
- [GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch](https://mlsanity.com/paper/2408.03204/) (2024)
- [NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects](https://mlsanity.com/paper/2502.11668/) (2025)
- [PyNeuralFx: A Python Package for Neural Audio Effect Modeling](https://mlsanity.com/paper/2408.06053/) (2024)

## Abstract (arXiv)

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.