Parametric Neural Amp Modeling with Active Learning
This work addresses the challenge of data-efficient virtual amp creation for audio processing applications, representing an incremental improvement in active learning for domain-specific tasks.
The paper tackles the problem of efficiently training parametric neural amp models by introducing PANAMA, an active learning framework that uses gradient-based optimization to select optimal amp knob settings, achieving effective modeling with a constrained number of samples.
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.