# CONMOD: Controllable Neural Frame-based Modulation Effects

arXiv: https://arxiv.org/abs/2406.13935 · HTML: https://mlsanity.com/paper/2406.13935/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Efficient neural networks for real-time modeling of analog dynamic range compression](https://mlsanity.com/paper/2102.06200/) (2021)
- [Sinusoidal Frequency Estimation by Gradient Descent](https://mlsanity.com/paper/2210.14476/) (2022)
- [Neural Music Synthesis for Flexible Timbre Control](https://mlsanity.com/paper/1811.00223/) (2018)
- [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)
- [Modulation Extraction for LFO-driven Audio Effects](https://mlsanity.com/paper/2305.13262/) (2023)
- [Differentiable Grey-box Modelling of Phaser Effects using Frame-based Spectral Processing](https://mlsanity.com/paper/2306.01332/) (2023)
- [Steerable discovery of neural audio effects](https://mlsanity.com/paper/2112.02926/) (2021)

## Built on by (most cited first, 1 of 1)

- [Gradient-based Optimisation of Modulation Effects](https://mlsanity.com/paper/2601.04867/) (2026)

## Abstract (arXiv)

Deep learning models have seen widespread use in modelling LFO-driven audio effects, such as phaser and flanger. Although existing neural architectures exhibit high-quality emulation of individual effects, they do not possess the capability to manipulate the output via control parameters. To address this issue, we introduce Controllable Neural Frame-based Modulation Effects (CONMOD), a single black-box model which emulates various LFO-driven effects in a frame-wise manner, offering control over LFO frequency and feedback parameters. Additionally, the model is capable of learning the continuous embedding space of two distinct phaser effects, enabling us to steer between effects and achieve creative outputs. Our model outperforms previous work while possessing both controllability and universality, presenting opportunities to enhance creativity in modern LFO-driven audio effects.