Neural Modelling of Time-Varying Effects
This paper proposes a grey-box neural network based approach
to modelling LFO modulated time-varying effects.
network model receives both the unprocessed audio, as well as
the LFO signal, as input. This allows complete control over the
model’s LFO frequency and shape. The neural networks are trained
using guitar audio, which has to be processed by the target effect
and also annotated with the predicted LFO signal before training.
A measurement signal based on regularly spaced chirps was used
to accurately predict the LFO signal. The model architecture has
been previously shown to be capable of running in real-time on a
modern desktop computer, whilst using relatively little processing
power. We validate our approach creating models of both a phaser
and a flanger effects pedal, and theoretically it can be applied to
any LFO modulated time-varying effect. In the best case, an errorto-signal ratio of 1.3% is achieved when modelling a flanger pedal,
and previous work has shown that this corresponds to the model
being nearly indistinguishable from the target device.