  Deep learning approaches have demonstrated success in modeling analog audio
effects. Nevertheless, challenges remain in modeling more complex effects that
involve time-varying nonlinear elements, such as dynamic range compressors.
Existing neural network approaches for modeling compression either ignore the
device parameters, do not attain sufficient accuracy, or otherwise require
large noncausal models prohibiting real-time operation. In this work, we
propose a modification to temporal convolutional networks (TCNs) enabling
greater efficiency without sacrificing performance. By utilizing very sparse
convolutional kernels through rapidly growing dilations, our model attains a
significant receptive field using fewer layers, reducing computation. Through a
detailed evaluation we demonstrate our efficient and causal approach achieves
state-of-the-art performance in modeling the analog LA-2A, is capable of
real-time operation on CPU, and only requires 10 minutes of training data.
