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

Efficient neural networks for real-time modeling of analog dynamic range compression