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.

翻译：移相器、镶边器与合唱效果等调制效果在电吉他演奏中被广泛使用。近年来，基于机器学习的模拟调制单元仿真技术已得到研究，但多数方法要么仅限于单一效果类别，要么相比经典数字实现方式存在计算成本高或延迟大的问题。本文在先前研究基础上，提出一种基于可微分数字信号处理的镶边、合唱与移相效果建模框架。该模型在时频域进行训练，但在推理时于时域运行，实现零延迟处理。我们探究了此类效果基于梯度优化所面临的挑战，并证明在损失函数中施加低频加权可避免学习延迟时间时陷入局部极小值。实验表明，当针对模拟效果单元进行训练时，模型的声音输出在某些情况下与参考信号在感知上难以区分，但对于长延迟时间与反馈效果仍存在优化挑战。