  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.
