mlsanityData to 14 Oct 2026

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

arXiv 2507.10534 ↗First posted 14 Jul 2025cs.SD cs.AIcited by 0

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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.

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.

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References · 22

  1. 2020DDSP: Differentiable Digital Signal Processingcited by 178
  2. 2019Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantitycited by 86
  3. 2025YuE: Scaling Open Foundation Models for Long-Form Music Generationcited by 71
  4. 2023SingSong: Generating musical accompaniments from singingcited by 55
  5. 2020Automatic multitrack mixing with a differentiable mixing console of neural audio effectscited by 27
  6. 2022Style Transfer of Audio Effects with Differentiable Signal Processingcited by 18
  7. 2022Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effectscited by 18
  8. 2024Presto! Distilling Steps and Layers for Accelerating Music Generationcited by 16
  9. 2025Fast Text-to-Audio Generation with Adversarial Post-Trainingcited by 14
  10. 2024DiffMoog: a Differentiable Modular Synthesizer for Sound Matchingcited by 7
  11. 2024ST-ITO: Controlling Audio Effects for Style Transfer with Inference-Time Optimizationcited by 7
  12. 2023Modulation Extraction for LFO-driven Audio Effectscited by 6
10 more
  1. 2025Differentiable Black-box and Gray-box Modeling of Nonlinear Audio Effectscited by 6
  2. 2023Blind Estimation of Audio Processing Graphcited by 5
  3. 2019SignalTrain: Profiling Audio Compressors with Deep Neural Networkscited by 4
  4. 2020Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networkscited by 4
  5. 2023InstrumentGen: Generating Sample-Based Musical Instruments From Textcited by 4
  6. 2023Singing Voice Synthesis Using Differentiable LPC and Glottal-Flow-Inspired Wavetablescited by 3
  7. 2023High-Fidelity Noise Reduction with Differentiable Signal Processingcited by 3
  8. 2024GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorchcited by 2
  9. 2025NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effectscited by 2
  10. 2024PyNeuralFx: A Python Package for Neural Audio Effect Modelingcited by 1

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