Active learning that maximizes ensemble disagreement across continuous amp knob settings reduces the number of recorded settings needed to train a parametric guitar amp model.
Parametric Neural Amp Modeling with Active Learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Parametric Neural Amp Modeling with Active Learning
Active learning that maximizes ensemble disagreement across continuous amp knob settings reduces the number of recorded settings needed to train a parametric guitar amp model.