Parametric Neural Amp Modeling with Active Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913923664445440 |
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| author | Grötschla, Florian Lanzendörfer, Luca A. Jiao, Longxiang Wattenhofer, Roger |
| author_facet | Grötschla, Florian Lanzendörfer, Luca A. Jiao, Longxiang Wattenhofer, Roger |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02109 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parametric Neural Amp Modeling with Active Learning Grötschla, Florian Lanzendörfer, Luca A. Jiao, Longxiang Wattenhofer, Roger Machine Learning Sound Audio and Speech Processing 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. |
| title | Parametric Neural Amp Modeling with Active Learning |
| topic | Machine Learning Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.02109 |