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S. Orcioni, A. Carini, S. Cecchi, A. Terenzi, and F. Piazza, "Identification of Nonlinear Audio Devices Exploiting Multiple-Variance Method and Perfect Sequences," Paper 9932, (2018 May.). doi: S. Orcioni, A. Carini, S. Cecchi, A. Terenzi, and F. Piazza, "Identification of Nonlinear Audio Devices Exploiting Multiple-Variance Method and Perfect Sequences," Paper 9932, (2018 May.). doi:
Abstract: Multiple-variance identification methods are based on the use of input signals with different powers for nonlinear system identification. They overcome the problem of the locality of the solution of traditional identification methods that well approximates the system only for inputs with approximately the same power of the identification signal. In this context, it is possible to further improve the nonlinear filter estimation exploiting as input signals the perfect periodic sequences that guarantee the orthogonality of the Wiener basis functions used for identification. Experimental results involving real measurements show that the proposed approach can accurately model nonlinear devices on a wide range of input variances. This property is particularly useful when modeling systems with high dynamic inputs, like audio amplifiers.

@article{orcioni2018identification,
author={orcioni, simone and carini, alberto and cecchi, stefania and terenzi, alessandro and piazza, francesco},
journal={journal of the audio engineering society},
title={identification of nonlinear audio devices exploiting multiple-variance method and perfect sequences},
year={2018},
volume={},
number={},
pages={},
doi={},
month={may},} @article{orcioni2018identification,
author={orcioni, simone and carini, alberto and cecchi, stefania and terenzi, alessandro and piazza, francesco},
journal={journal of the audio engineering society},
title={identification of nonlinear audio devices exploiting multiple-variance method and perfect sequences},
year={2018},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={multiple-variance identification methods are based on the use of input signals with different powers for nonlinear system identification. they overcome the problem of the locality of the solution of traditional identification methods that well approximates the system only for inputs with approximately the same power of the identification signal. in this context, it is possible to further improve the nonlinear filter estimation exploiting as input signals the perfect periodic sequences that guarantee the orthogonality of the wiener basis functions used for identification. experimental results involving real measurements show that the proposed approach can accurately model nonlinear devices on a wide range of input variances. this property is particularly useful when modeling systems with high dynamic inputs, like audio amplifiers.},}

TY - paper
TI - Identification of Nonlinear Audio Devices Exploiting Multiple-Variance Method and Perfect Sequences
SP - EP -
AU - Orcioni, Simone
AU - Carini, Alberto
AU - Cecchi, Stefania
AU - Terenzi, Alessandro
AU - Piazza, Francesco
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2018 TY - paper
TI - Identification of Nonlinear Audio Devices Exploiting Multiple-Variance Method and Perfect Sequences
SP - EP -
AU - Orcioni, Simone
AU - Carini, Alberto
AU - Cecchi, Stefania
AU - Terenzi, Alessandro
AU - Piazza, Francesco
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2018
AB - Multiple-variance identification methods are based on the use of input signals with different powers for nonlinear system identification. They overcome the problem of the locality of the solution of traditional identification methods that well approximates the system only for inputs with approximately the same power of the identification signal. In this context, it is possible to further improve the nonlinear filter estimation exploiting as input signals the perfect periodic sequences that guarantee the orthogonality of the Wiener basis functions used for identification. Experimental results involving real measurements show that the proposed approach can accurately model nonlinear devices on a wide range of input variances. This property is particularly useful when modeling systems with high dynamic inputs, like audio amplifiers.