# Steerable discovery of neural audio effects

arXiv: https://arxiv.org/abs/2112.02926 · HTML: https://mlsanity.com/paper/2112.02926/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2021.

## Built on by (most cited first, 3 of 3)

- [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)
- [Evaluating Neural Networks Architectures for Spring Reverb Modelling](https://mlsanity.com/paper/2409.04953/) (2024)

## Abstract (arXiv)

Applications of deep learning for audio effects often focus on modeling analog effects or learning to control effects to emulate a trained audio engineer. However, deep learning approaches also have the potential to expand creativity through neural audio effects that enable new sound transformations. While recent work demonstrated that neural networks with random weights produce compelling audio effects, control of these effects is limited and unintuitive. To address this, we introduce a method for the steerable discovery of neural audio effects. This method enables the design of effects using example recordings provided by the user. We demonstrate how this method produces an effect similar to the target effect, along with interesting inaccuracies, while also providing perceptually relevant controls.