Objective and subjective comparison of several machine learning techniques applied to the real-time emulation of the guitar amplifier nonlinear behavior

Recent progresses made in the nonlinear system identification field have improved the ability to emulate nonlinear audio systems such as the tube guitar amplifiers. In particular, machine learning techniques have enabled an accurate emulation of such devices. The next challenge lies in the ability to reduce the computation time of these models. The first purpose of this paper is to compare different neural-network architectures in terms of accuracy and computation time. The second purpose is to select the fastest model keeping the same perceived accuracy using a subjective evaluation of the model with a listening-test.