# Modulation Extraction for LFO-driven Audio Effects

arXiv: https://arxiv.org/abs/2305.13262 · HTML: https://mlsanity.com/paper/2305.13262/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 10 of 10)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Efficient neural networks for real-time modeling of analog dynamic range compression](https://mlsanity.com/paper/2102.06200/) (2021)
- [Modelling black-box audio effects with time-varying feature modulation](https://mlsanity.com/paper/2211.00497/) (2022)
- [A general-purpose deep learning approach to model time-varying audio effects](https://mlsanity.com/paper/1905.06148/) (2019)
- [A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor](https://mlsanity.com/paper/1905.01022/) (2019)
- [Blind Estimation of Audio Processing Graph](https://mlsanity.com/paper/2303.08610/) (2023)
- [Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/2012.03216/) (2020)

## Built on by (most cited first, 6 of 6)

- [Differentiable All-pole Filters for Time-varying Audio Systems](https://mlsanity.com/paper/2404.07970/) (2024)
- [CONMOD: Controllable Neural Frame-based Modulation Effects](https://mlsanity.com/paper/2406.13935/) (2024)
- [Modulation Discovery with Differentiable Digital Signal Processing](https://mlsanity.com/paper/2510.06204/) (2025)
- [WildFX: A DAW-Powered Pipeline for In-the-Wild Audio FX Graph Modeling](https://mlsanity.com/paper/2507.10534/) (2025)
- [Time-Varying Audio Effect Modeling by End-to-End Adversarial Training](https://mlsanity.com/paper/2512.15313/) (2025)
- [Gradient-based Optimisation of Modulation Effects](https://mlsanity.com/paper/2601.04867/) (2026)

## Abstract (arXiv)

Low frequency oscillator (LFO) driven audio effects such as phaser, flanger, and chorus, modify an input signal using time-varying filters and delays, resulting in characteristic sweeping or widening effects. It has been shown that these effects can be modeled using neural networks when conditioned with the ground truth LFO signal. However, in most cases, the LFO signal is not accessible and measurement from the audio signal is nontrivial, hindering the modeling process. To address this, we propose a framework capable of extracting arbitrary LFO signals from processed audio across multiple digital audio effects, parameter settings, and instrument configurations. Since our system imposes no restrictions on the LFO signal shape, we demonstrate its ability to extract quasiperiodic, combined, and distorted modulation signals that are relevant to effect modeling. Furthermore, we show how coupling the extraction model with a simple processing network enables training of end-to-end black-box models of unseen analog or digital LFO-driven audio effects using only dry and wet audio pairs, overcoming the need to access the audio effect or internal LFO signal. We make our code available and provide the trained audio effect models in a real-time VST plugin.