# Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models

arXiv: https://arxiv.org/abs/2411.14972 · HTML: https://mlsanity.com/paper/2411.14972/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 22 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch](https://mlsanity.com/paper/2310.17864/) (2023)
- [Deep Learning for Tube Amplifier Emulation](https://mlsanity.com/paper/1811.00334/) (2018)
- [Style Transfer of Audio Effects with Differentiable Signal Processing](https://mlsanity.com/paper/2207.08759/) (2022)
- [Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects](https://mlsanity.com/paper/2211.02247/) (2022)
- [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)
- [towards automatic transcription of polyphonic electric guitar music:a new dataset and a multi-loss transformer model](https://mlsanity.com/paper/2202.09907/) (2022)
- [General Purpose Audio Effect Removal](https://mlsanity.com/paper/2308.16177/) (2023)
- [SynthTab: Leveraging Synthesized Data for Guitar Tablature Transcription](https://mlsanity.com/paper/2309.09085/) (2023)
- [Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing](https://mlsanity.com/paper/2406.06293/) (2024)
- [End-to-end Music Remastering System Using Self-supervised and Adversarial Training](https://mlsanity.com/paper/2202.08520/) (2022)
- [Differentiable Grey-box Modelling of Phaser Effects using Frame-based Spectral Processing](https://mlsanity.com/paper/2306.01332/) (2023)
- [End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models](https://mlsanity.com/paper/2403.08559/) (2024)
- [Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/2012.03216/) (2020)
- [Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control](https://mlsanity.com/paper/2407.10646/) (2024)
- [Neural modeling of magnetic tape recorders](https://mlsanity.com/paper/2305.16862/) (2023)
- [Distortion Recovery: A Two-Stage Method for Guitar Effect Removal](https://mlsanity.com/paper/2407.16639/) (2024)

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

- [Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches](https://mlsanity.com/paper/2504.04751/) (2025)
- [Resampling Filter Design for Multirate Neural Audio Effect Processing](https://mlsanity.com/paper/2501.18470/) (2025)
- [Anti-aliasing of neural distortion effects via model fine tuning](https://mlsanity.com/paper/2505.11375/) (2025)

## Abstract (arXiv)

This paper introduces Open-Amp, a synthetic data framework for generating large-scale and diverse audio effects data. Audio effects are relevant to many musical audio processing and Music Information Retrieval (MIR) tasks, such as modelling of analog audio effects, automatic mixing, tone matching and transcription. Existing audio effects datasets are limited in scope, usually including relatively few audio effects processors and a limited amount of input audio signals. Our proposed framework overcomes these issues, by crowdsourcing neural network emulations of guitar amplifiers and effects, created by users of open-source audio effects emulation software. This allows users of Open-Amp complete control over the input signals to be processed by the effects models, as well as providing high-quality emulations of hundreds of devices. Open-Amp can render audio online during training, allowing great flexibility in data augmentation. Our experiments show that using Open-Amp to train a guitar effects encoder achieves new state-of-the-art results on multiple guitar effects classification tasks. Furthermore, we train a one-to-many guitar effects model using Open-Amp, and use it to emulate unseen analog effects via manipulation of its learned latent space, indicating transferability to analog guitar effects data.