real time emulation; neural network; nonlinear model
Abstract :
[en] Numerous audio systems for musicians are expensive and bulky. Therefore, it could be advantageous to model
them and to replace them by computer emulation. Their nonlinear behavior requires the use of complex models.
We propose to take advantage of the progress made in the field of machine learning to build a new model for such
nonlinear audio devices (such as the tube amplifier). This paper specially focuses on the real-time constraints of the
model. Modifying the structure of the Long Short Term Memory neural-network has led to a model 10 times faster
while keeping a very good accuracy. Indeed, the root mean square error between the signal coming from the tube
amplifier and the output of the neural network is around 2%.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Schmitz, Thomas ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
Embrechts, Jean-Jacques ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Techniques du son et de l'image
Language :
English
Title :
Nonlinear Real-Time Emulation of a Tube Amplifier with a Long Short Term Memory Neural-Network
Alternative titles :
[en] Emulation en temps réel d'un amplificateur à tube avec un réseau de neurones Long Short Term Memory (LSTM)
