用主动学习精简吉他音箱建模数据，高效生成虚拟音箱
Parametric Neural Amp Modeling with Active Learning
- 基于梯度优化选择最优音量旋钮配置采样点
- 仅需少量参数设置即可训练出高质量虚拟音箱
- 适合资源受限场景下的音乐效果器建模
我们提出 PANAMA，一种基于主动学习的端到端参数化吉他音箱建模框架，采用类似 WaveNet 的架构。通过该模型，仅需最少数量的音量旋钮设置（即数据点）即可创建虚拟音箱。我们证明了基于梯度的优化算法可用于确定最优采样点，在样本数量受限的情况下仍能有效提升建模性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。