# End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models

arXiv: https://arxiv.org/abs/2403.08559 · HTML: https://mlsanity.com/paper/2403.08559/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2024.

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

- [Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models](https://mlsanity.com/paper/2411.14972/) (2024)
- [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)
- [Parametric Neural Amp Modeling with Active Learning](https://mlsanity.com/paper/2507.02109/) (2025)
- [Parametric Neural Amp Modeling with Active Learning](https://mlsanity.com/paper/2509.26564/) (2025)

## Abstract (arXiv)

This paper describes a data-driven approach to creating real-time neural network models of guitar amplifiers, recreating the amplifiers' sonic response to arbitrary inputs at the full range of controls present on the physical device. While the focus on the paper is on the data collection pipeline, we demonstrate the effectiveness of this conditioned black-box approach by training an LSTM model to the task, and comparing its performance to an offline white-box SPICE circuit simulation. Our listening test results demonstrate that the neural amplifier modeling approach can match the subjective performance of a high-quality SPICE model, all while using an automated, non-intrusive data collection process, and an end-to-end trainable, real-time feasible neural network model.