用主动学习精简采样，高效训练可调音色的虚拟吉他音箱模型。
Parametric Neural Amp Modeling with Active Learning
- 结合LSTM与WaveNet结构，通过集成学习主动挑选最有信息量的采样点。
- 仅需75个参数设置样本，音质接近顶尖非参数模型NAM。
- 适合音乐制作人和音频工程师快速构建个性化虚拟音箱。
我们提出Panama，一种基于主动学习的端到端训练框架，用于构建可调参数的吉他音箱模型。该框架融合LSTM与类似WaveNet的架构，采用基于集成学习的主动采样策略，通过梯度优化最大化模型间分歧，从而识别最具信息量的采样点（即音箱旋钮设置）。在MUSHRA听觉测试中，仅使用75个数据点，模型即可达到与领先开源非参数模型NAM相当的感知音质水平。
原文摘要 · Abstract (English)
We introduce Panama, an active learning framework to train parametric guitar amp models end-to-end using a combination of an LSTM model and a WaveNet-like architecture. With \model, one can create a virtual amp by recording samples that are determined through an ensemble-based active learning strategy to minimize the amount of datapoints needed (i.e., amp knob settings). Our strategy uses gradient-based optimization to maximize the disagreement among ensemble models, in order to identify the most informative datapoints. MUSHRA listening tests reveal that, with 75 datapoints, our models are able to match the perceptual quality of NAM, the leading open-source non-parametric amp modeler.
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