  Deep learning approaches have demonstrated success in the task of modeling
analog audio effects such as distortion and overdrive. Nevertheless, challenges
remain in modeling more complex effects, such as dynamic range compressors,
along with their variable parameters. Previous methods are computationally
complex, and noncausal, prohibiting real-time operation, which is critical for
use in audio production contexts. They additionally utilize large training
datasets, which are time-intensive to generate. In this work, we demonstrate
that shallower temporal convolutional networks (TCNs) that exploit very large
dilation factors for significant receptive field can achieve state-of-the-art
performance, while remaining efficient. Not only are these models found to be
perceptually similar to the original effect, they achieve a 4x speedup,
enabling real-time operation on CPU, and can be trained using only 1% of the
data from previous methods.
