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T. Schmitz, and J. Embrechts, "Objective and Subjective Comparison of Several Machine Learning Techniques Applied for the Real-Time Emulation of the Guitar Amplifier Nonlinear Behavior," Paper 10191, (2019 March.). doi: T. Schmitz, and J. Embrechts, "Objective and Subjective Comparison of Several Machine Learning Techniques Applied for the Real-Time Emulation of the Guitar Amplifier Nonlinear Behavior," Paper 10191, (2019 March.). doi:
Abstract: 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.

@article{schmitz2019objective,
author={schmitz, thomas and embrechts, jean-jacques},
journal={journal of the audio engineering society},
title={objective and subjective comparison of several machine learning techniques applied for the real-time emulation of the guitar amplifier nonlinear behavior},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},} @article{schmitz2019objective,
author={schmitz, thomas and embrechts, jean-jacques},
journal={journal of the audio engineering society},
title={objective and subjective comparison of several machine learning techniques applied for the real-time emulation of the guitar amplifier nonlinear behavior},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},
abstract={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.},}

TY - paper
TI - Objective and Subjective Comparison of Several Machine Learning Techniques Applied for the Real-Time Emulation of the Guitar Amplifier Nonlinear Behavior
SP - EP -
AU - Schmitz, Thomas
AU - Embrechts, Jean-Jacques
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019 TY - paper
TI - Objective and Subjective Comparison of Several Machine Learning Techniques Applied for the Real-Time Emulation of the Guitar Amplifier Nonlinear Behavior
SP - EP -
AU - Schmitz, Thomas
AU - Embrechts, Jean-Jacques
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019
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.