# Blind Estimation of Audio Processing Graph

arXiv: https://arxiv.org/abs/2303.08610 · HTML: https://mlsanity.com/paper/2303.08610/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [Modulation Extraction for LFO-driven Audio Effects](https://mlsanity.com/paper/2305.13262/) (2023)
- [Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches](https://mlsanity.com/paper/2504.04751/) (2025)
- [Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior](https://mlsanity.com/paper/2505.11315/) (2025)
- [WildFX: A DAW-Powered Pipeline for In-the-Wild Audio FX Graph Modeling](https://mlsanity.com/paper/2507.10534/) (2025)
- [Music Restoration via Latent Operator Optimization and Diffusion Model Priors](https://mlsanity.com/paper/2608.01972/) (2026)

## Abstract (arXiv)

Musicians and audio engineers sculpt and transform their sounds by connecting multiple processors, forming an audio processing graph. However, most deep-learning methods overlook this real-world practice and assume fixed graph settings. To bridge this gap, we develop a system that reconstructs the entire graph from a given reference audio. We first generate a realistic graph-reference pair dataset and train a simple blind estimation system composed of a convolutional reference encoder and a transformer-based graph decoder. We apply our model to singing voice effects and drum mixing estimation tasks. Evaluation results show that our method can reconstruct complex signal routings, including multi-band processing and sidechaining.