Real Time Emulation of Parametric Guitar Tube Amplifier with Long Short Term Memory Neural Network

Numerous audio systems for musicians are expensive and bulky. Therefore, it could be advantageous to
model them and to replace them by computer emulation. In guitar players’ world, audio systems could have
a desirable nonlinear behavior (distortion effects). It is thus difficult to find a simple model to emulate them
in real time. Volterra series model and its subclass are usual ways to model nonlinear systems.
Unfortunately, these systems are difficult to identify in an analytic way. In this paper we propose to take
advantage of the new progress made in neural networks to emulate them in real time. We show that an
accurate emulation can be reached with less than 1% of root mean square error between the signal coming
from a tube amplifier and the output of the neural network. Moreover, the research has been extended to
model the Gain parameter of the amplifier.