Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method

Orcioni S, Terenzi A, Cecchi S, Piazza F, Carini A. Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method. J Audio Eng Soc. 2018;66(10):823-838. doi:10.17743/jaes.2018.0046

@article{Orcioni2018_19864,
  author = {Orcioni, Simone and Terenzi, Alessandro and Cecchi, Stefania and Piazza, Francesco and Carini, Alberto},
  title = {{Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method}},
  journal = {Journal of the Audio Engineering Society},
  volume = {66},
  number = {10},
  pages = {823--838},
  year = {2018},
  month = oct,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2018.0046},
  url = {https://doi.org/10.17743/jaes.2018.0046}
}

TY  - JOUR
TI  - Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method
AU  - Orcioni, Simone
AU  - Terenzi, Alessandro
AU  - Cecchi, Stefania
AU  - Piazza, Francesco
AU  - Carini, Alberto
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 66
IS  - 10
SP  - 823
EP  - 838
PY  - 2018
DA  - 2018/10/06
DO  - 10.17743/jaes.2018.0046
UR  - https://doi.org/10.17743/jaes.2018.0046
PB  - Audio Engineering Society
LA  - en
AB  - The multiple-variance method is a cross-correlation method that exploits input signals with different powers for the identification of a nonlinear system by means of the Volterra series. It overcomes the problem of the locality of the solution of traditional nonlinear identification methods that successfully approximate only for inputs having approximately the same power of the identification signal. The multiple-variance method improves the model performance in case of inputs with high dynamic range. This method is used to identify three different tube amplifiers, and it is applied to a novel reduced Volterra model. This overcomes the problem of the very large number of coefficients required by the Volterra series, the so-called “course of dimensionality.” The paper demonstrates the effectiveness of the multiple-variance methodology in terms of system identification error and computational complexity.
ER  -