用可微信号处理建模吉他混响效果，训练快推理零延迟。
Gradient-based Optimisation of Modulation Effects
- 基于可微数字信号处理，端到端学习调制效果
- 在时间-频域训练，推理时零延迟，适合实时应用
- 低频加权损失避免局部最优，对长延时仍存挑战
相位器、镶边和合唱等调制效果在电吉他中广泛应用。近年来已有基于机器学习的模拟方法，但多数仅限于单一类型，或计算成本高、延迟大。本文基于前人工作，提出一种基于可微数字信号处理的框架，用于建模镶边、合唱与相位器效果。模型在时间-频域进行训练，但在推理阶段运行于时域，实现零延迟。我们探讨了梯度优化此类效果的挑战，发现对损失函数施加低频加权可避免学习延迟时间时陷入局部最优。当以模拟设备为参考进行训练时，模型输出在某些情况下听觉上无法区分于原始效果，但长延迟时间和反馈结构仍存在建模难题。
原文摘要 · Abstract (English)
Modulation effects such as phasers, flangers and chorus effects are heavily used in conjunction with the electric guitar. Machine learning based emulation of analog modulation units has been investigated in recent years, but most methods have either been limited to one class of effect or suffer from a high computational cost or latency compared to canonical digital implementations. Here, we build on previous work and present a framework for modelling flanger, chorus and phaser effects based on differentiable digital signal processing. The model is trained in the time-frequency domain, but at inference operates in the time-domain, requiring zero latency. We investigate the challenges associated with gradient-based optimisation of such effects, and show that low-frequency weighting of loss functions avoids convergence to local minima when learning delay times. We show that when trained against analog effects units, sound output from the model is in some cases perceptually indistinguishable from the reference, but challenges still remain for effects with long delay times and feedback.
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