Anti-Aliasing of Neural Distortion Effects via Model Fine Tuning
Neural networks have become ubiquitous with guitar distortion
effects modelling in recent years. Despite their ability to yield
perceptually convincing models, they are susceptible to frequency
aliasing when driven by high frequency and high gain inputs.
Nonlinear activation functions create both the desired harmonic
distortion and unwanted aliasing distortion as the bandwidth of
the signal is expanded beyond the Nyquist frequency. Here, we
present a method for reducing aliasing in neural models via a
teacher-student fine tuning approach, where the teacher is a pretrained model with its weights frozen, and the student is a copy of
this with learnable parameters. The student is fine-tuned against
an aliasing-free dataset generated by passing sinusoids through
the original model and removing non-harmonic components from
the output spectra.
Our results show that this method significantly suppresses aliasing for both long-short-term-memory networks (LSTM) and temporal convolutional networks (TCN). In the
majority of our case studies, the reduction in aliasing was greater
than that achieved by two times oversampling. One side-effect
of the proposed method is that harmonic distortion components
are also affected.
This adverse effect was found to be modeldependent, with the LSTM models giving the best balance between
anti-aliasing and preserving the perceived similarity to an analog
reference device.