Nonlinear Real-Time Emulation of a Tube Amplifier with a Long Short Time Memory Neural-Network

Schmitz T, Embrechts JJ. Nonlinear Real-Time Emulation of a Tube Amplifier with a Long Short Time Memory Neural-Network. In: AES Convention 144. Audio Engineering Society; 2018. Paper 9966. Available from: https://aes.org/publications/elibrary-page/?id=19483

@inproceedings{Schmitz2018_19483,
  author = {Schmitz, Thomas and Embrechts, Jean-Jacques},
  title = {{Nonlinear Real-Time Emulation of a Tube Amplifier with a Long Short Time Memory Neural-Network}},
  booktitle = {AES Convention 144},
  note = {Paper 9966},
  year = {2018},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=19483}
}

TY  - CPAPER
TI  - Nonlinear Real-Time Emulation of a Tube Amplifier with a Long Short Time Memory Neural-Network
AU  - Schmitz, Thomas
AU  - Embrechts, Jean-Jacques
T2  - AES Convention 144
M1  - Paper 9966
PY  - 2018
DA  - 2018/05/06
UR  - https://aes.org/publications/elibrary-page/?id=19483
PB  - Audio Engineering Society
LA  - en
AB  - 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%.
ER  -