Neural Modeling of Phaser and Flanging Effects

This article further explores a previously proposed gray-box neural network approach to modeling LFO (low-frequency oscillator) modulated time-varying audio effects. The network inputs are both the unprocessed audio and LFO signal. This allows the LFO to be freely controlled after model training. This paper introduces an improved process for accurately measuring the frequency response of a time-varying system over time, which is used to annotate the neural network training data with the LFO of the effect being modeled. Accuracy is improved by using a frequency domain synthesized chirp signal and using shorter and more closely spaced chirps. A digital flanger effect is used to test the accuracy of the method and neural network models of two guitar effects pedals, a phaser and flanger, were created. The improvement in the system measurement method is reflected in the accuracy of the resulting models, which significantly outperform previously reported results. When modeling a phaser and flanger pedal, error-to-signal ratios of 0.2% and 0.3% were achieved, respectively. Previous work suggests errors of this size are often inaudible. The model architecture can run in real time on a modern computer while using relatively little processing power.