Real Time Emulation of Parametric Guitar Tube Amplifier With Long Short Term Memory Neural Network
This addresses the need for affordable and portable audio systems for guitar players by replacing expensive hardware with computer emulation.
The paper tackled the problem of emulating nonlinear guitar tube amplifiers in real time, achieving accurate emulation with less than 1% root mean square error compared to the actual amplifier signal.
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.