Parametric Neural Amp Modeling with Active Learning
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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.
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.
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References · 12
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
- 2023High-Fidelity Audio Compression with Improved RVQGANcited by 431
- 2018Deep Learning for Tube Amplifier Emulationcited by 18
- 2021Efficient neural networks for real-time modeling of analog dynamic range compressioncited by 15
- 2022Modelling black-box audio effects with time-varying feature modulationcited by 11
- 2019A general-purpose deep learning approach to model time-varying audio effectscited by 8
- 2024Sample Rate Independent Recurrent Neural Networks for Audio Effects Processingcited by 7
- 2025Differentiable Black-box and Gray-box Modeling of Nonlinear Audio Effectscited by 6
- 2024End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Modelscited by 5
- 2018Real Time Emulation of Parametric Guitar Tube Amplifier With Long Short Term Memory Neural Networkcited by 2
- 2024Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modelingcited by 2
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