Differentiable Grey-box Modelling of Phaser Effects using Frame-based Spectral Processing
This work addresses a specific problem in audio effect modeling for music production and digital signal processing, offering an incremental improvement over existing methods by enabling causal and interpretable phaser emulation.
The authors tackled the challenge of modeling phaser audio effects, which involve time-varying filters controlled by low-frequency oscillators, by developing a differentiable grey-box model that jointly learns the control signal and spectral response, achieving accurate emulation of an analog reference device with adjustable parameters.
Machine learning approaches to modelling analog audio effects have seen intensive investigation in recent years, particularly in the context of non-linear time-invariant effects such as guitar amplifiers. For modulation effects such as phasers, however, new challenges emerge due to the presence of the low-frequency oscillator which controls the slowly time-varying nature of the effect. Existing approaches have either required foreknowledge of this control signal, or have been non-causal in implementation. This work presents a differentiable digital signal processing approach to modelling phaser effects in which the underlying control signal and time-varying spectral response of the effect are jointly learned. The proposed model processes audio in short frames to implement a time-varying filter in the frequency domain, with a transfer function based on typical analog phaser circuit topology. We show that the model can be trained to emulate an analog reference device, while retaining interpretable and adjustable parameters. The frame duration is an important hyper-parameter of the proposed model, so an investigation was carried out into its effect on model accuracy. The optimal frame length depends on both the rate and transient decay-time of the target effect, but the frame length can be altered at inference time without a significant change in accuracy.