通过平滑激活函数降低神经放大器模型的混叠失真
Aliasing Reduction in Neural Amp Modeling by Smoothing Activations
- 设计更平滑的激活函数以减少非线性带来的混叠
- 新指标ASR显示平滑函数可显著降低混叠水平
- 在保持高建模精度的同时有效抑制失真，适合音频建模研究者
对高质量数字版模拟音频设备（如经典电子管吉他放大器）的需求推动了基于神经网络的黑箱建模发展，深度学习架构如WaveNet已展现良好效果。然而，所有模型均面临由神经网络非线性激活函数引发的混叠伪影问题。本文研究了新型及改进的激活函数，旨在减轻神经放大器模型中的混叠现象。为此，提出了新的量化指标——混叠-信号比（ASR），可高精度评估混叠程度。同时测量传统误差-信号比（ESR），在多种现有与现代激活函数上进行测试，比较不同拉伸因子下的表现。结果表明，具有更平滑曲线的激活函数能显著降低ASR值，且未明显增加ESR，证明在保持高建模精度的同时可有效减少混叠，为神经放大器建模提供了可行优化路径。
原文摘要 · Abstract (English)
The increasing demand for high-quality digital emulations of analog audio hardware, such as vintage tube guitar amplifiers, led to numerous works on neural network-based black-box modeling, with deep learning architectures like WaveNet showing promising results. However, a key limitation in all of these models was the aliasing artifacts stemming from nonlinear activation functions in neural networks. In this paper, we investigated novel and modified activation functions aimed at mitigating aliasing within neural amplifier models. Supporting this, we introduced a novel metric, the Aliasing-to-Signal Ratio (ASR), which quantitatively assesses the level of aliasing with high accuracy. Measuring also the conventional Error-to-Signal Ratio (ESR), we conducted studies on a range of preexisting and modern activation functions with varying stretch factors. Our findings confirmed that activation functions with smoother curves tend to achieve lower ASR values, indicating a noticeable reduction in aliasing. Notably, this improvement in aliasing reduction was achievable without a substantial increase in ESR, demonstrating the potential for high modeling accuracy with reduced aliasing in neural amp models.
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