In this paper we present an approach to using traditional digital IIR
filter structures inside deep-learning networks trained using backpropagation. We establish the link between such structures and
recurrent neural networks. Three different differentiable IIR filter
topologies are presented and compared against each other and an
established baseline. Additionally, a simple Wiener-Hammerstein
model using differentiable IIRs as its filtering component is presented and trained on a guitar signal played through a Boss DS-1
guitar pedal.