Deep learning approaches for black-box modelling of audio effects have shown
promise, however, the majority of existing work focuses on nonlinear effects
with behaviour on relatively short time-scales, such as guitar amplifiers and
distortion. While recurrent and convolutional architectures can theoretically
be extended to capture behaviour at longer time scales, we show that simply
scaling the width, depth, or dilation factor of existing architectures does not
result in satisfactory performance when modelling audio effects such as fuzz
and dynamic range compression. To address this, we propose the integration of
time-varying feature-wise linear modulation into existing temporal
convolutional backbones, an approach that enables learnable adaptation of the
intermediate activations. We demonstrate that our approach more accurately
captures long-range dependencies for a range of fuzz and compressor
implementations across both time and frequency domain metrics. We provide sound
examples, source code, and pretrained models to faciliate reproducibility.

该研究提出了一种将时间变化的特征线性调制整合进现有的时间卷积背骨架中的方法，以更准确地捕获长时间尺度上的音频效应，从而增强深度学习方法对音频效应的建模能力。