Objective and Subjective Comparison of Several Machine Learning Techniques Applied for the Real-Time Emulation of the Guitar Amplifier Nonlinear Behavior

Schmitz T, Embrechts JJ. Objective and Subjective Comparison of Several Machine Learning Techniques Applied for the Real-Time Emulation of the Guitar Amplifier Nonlinear Behavior. In: AES Convention 146. Audio Engineering Society; 2019. Paper 10191. Available from: https://aes.org/publications/elibrary-page/?id=20324

@inproceedings{Schmitz2019_20324,
  author = {Schmitz, Thomas and Embrechts, Jean-Jacques},
  title = {{Objective and Subjective Comparison of Several Machine Learning Techniques Applied for the Real-Time Emulation of the Guitar Amplifier Nonlinear Behavior}},
  booktitle = {AES Convention 146},
  note = {Paper 10191},
  year = {2019},
  month = mar,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20324}
}

TY  - CPAPER
TI  - Objective and Subjective Comparison of Several Machine Learning Techniques Applied for the Real-Time Emulation of the Guitar Amplifier Nonlinear Behavior
AU  - Schmitz, Thomas
AU  - Embrechts, Jean-Jacques
T2  - AES Convention 146
M1  - Paper 10191
PY  - 2019
DA  - 2019/03/06
UR  - https://aes.org/publications/elibrary-page/?id=20324
PB  - Audio Engineering Society
LA  - en
AB  - Recent progress made in the nonlinear system identification field have improved the ability to emulate nonlinear audio systems such as the tube guitar amplifiers. In particular, machine learning techniques have enabled an accurate emulation of such devices. The next challenge lies in the ability to reduce the computation time of these models. The first purpose of this paper is to compare different neural-network architectures in terms of accuracy and computation time. The second purpose is to select the fastest model keeping the same perceived accuracy using a subjective evaluation of the model with a listening-test.
ER  -