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.
Cite
@article{arxiv.2507.02109,
title = {Parametric Neural Amp Modeling with Active Learning},
author = {Florian Grötschla and Luca A. Lanzendörfer and Longxiang Jiao and Roger Wattenhofer},
journal= {arXiv preprint arXiv:2507.02109},
year = {2025}
}
Comments
Accepted at ISMIR 2025 as Late-Breaking Demo (LBD)