SignalTrain: Modeling Time-dependent Nonlinear Signal Processing Effects Using Deep Neural Networks

The advent of increased consumer computing power and graphics processing unit (GPU) usage over the last decade has made possible machine learning approaches to modeling problems once thought impractical.This work expands on prior research published on modeling nonlinear time-dependent signal processing effects associated with music production by means of a deep neural network¹. The presented results show the progress in accurately modeling these effects through architecture and optimization changes, increasing computational efficiency, lowering noise, and extending to a larger variety of nonlinear audio effects. Unique contributions of this effort include the ability to emulate the individual settings or “knobs” you would see on an analog piece of equipment, and with the production of commercially viable audio, i.e. 44.1kHz sampling rate at 16-bit resolution.

[1] S.H. Hawley, et al “Profiling Audio Compressors with Deep Neural Networks” Audio Engineering Society 147^th Convention, New York City, New York. Oct 2019