Low frequency oscillator (LFO) driven audio effects such as phaser, flanger,
and chorus, modify an input signal using time-varying filters and delays,
resulting in characteristic sweeping or widening effects. It has been shown
that these effects can be modeled using neural networks when conditioned with
the ground truth LFO signal. However, in most cases, the LFO signal is not
accessible and measurement from the audio signal is nontrivial, hindering the
modeling process. To address this, we propose a framework capable of extracting
arbitrary LFO signals from processed audio across multiple digital audio
effects, parameter settings, and instrument configurations. Since our system
imposes no restrictions on the LFO signal shape, we demonstrate its ability to
extract quasiperiodic, combined, and distorted modulation signals that are
relevant to effect modeling. Furthermore, we show how coupling the extraction
model with a simple processing network enables training of end-to-end black-box
models of unseen analog or digital LFO-driven audio effects using only dry and
wet audio pairs, overcoming the need to access the audio effect or internal LFO
signal. We make our code available and provide the trained audio effect models
in a real-time VST plugin.

本文提出了一个基于神经网络的提取任意低频振荡信号的框架，该框架能够跨越多种数字音频效果器、参数设置和乐器配置，提取不受限制的低频振荡信号。此外，本文还展示了如何将提取模型与一个简单的处理网络相结合，仅使用干燥信号和混响信号对未见过的模拟或数字低频振荡驱动音频效果进行端到端的建模。