# Parametric Neural Amp Modeling with Active Learning

arXiv: https://arxiv.org/abs/2507.02109 · HTML: https://mlsanity.com/paper/2507.02109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [High-Fidelity Audio Compression with Improved RVQGAN](https://mlsanity.com/paper/2306.06546/) (2023)
- [Deep Learning for Tube Amplifier Emulation](https://mlsanity.com/paper/1811.00334/) (2018)
- [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)
- [Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing](https://mlsanity.com/paper/2406.06293/) (2024)
- [Differentiable Black-box and Gray-box Modeling of Nonlinear Audio Effects](https://mlsanity.com/paper/2502.14405/) (2025)
- [End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models](https://mlsanity.com/paper/2403.08559/) (2024)
- [Real Time Emulation of Parametric Guitar Tube Amplifier With Long Short Term Memory Neural Network](https://mlsanity.com/paper/1804.07145/) (2018)
- [Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling](https://mlsanity.com/paper/2408.04829/) (2024)

## Abstract (arXiv)

We introduce PANAMA, an active learning framework for the training of end-to-end parametric guitar amp models using a WaveNet-like architecture. With model, one can create a virtual amp by recording samples that are determined by an active learning strategy to use a minimum amount of datapoints (i.e., amp knob settings). We show that gradient-based optimization algorithms can be used to determine the optimal datapoints to sample, and that the approach helps under a constrained number of samples.