A Differentiable Digital Moog Filter For Machine Learning Applications

Etienne Gerat; Purbaditya Bhattacharya; Udo Zoelzer
DAFx-2023 - Copenhagen
In this project, a digital ladder filter has been investigated and expanded. This structure is a simplified digital analog model of the well known analog Moog ladder filter. The goal of this paper is to derive the differentiation expressions of this filter with respect to its control parameters in order to integrate it in machine learning systems. The derivation of the backpropagation method is described in this work, it can be generalized to a Moog filter or a similar filter having any number of stages. Subsequently, the example of an adaptive Moog filter is provided. Finally, a machine learning application example is shown where the filter is integrated in a deep learning framework.