Efficient neural networks for real-time modeling of analog dynamic range compression
This work addresses the challenge of real-time, accurate modeling of complex analog audio effects for audio processing applications, representing an incremental improvement over existing methods.
The authors tackled the problem of modeling analog dynamic range compression with neural networks, achieving state-of-the-art performance on the LA-2A compressor while enabling real-time CPU operation and requiring only 10 minutes of training data.
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.