Parametric Neural Amp Modeling with Active Learning

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.

@article{grötschla2025_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 }
}