  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.
