Efficient Neural Networks for Real-time Analog Audio Effect Modeling

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.