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Identification de systèmes non linéaires représentés en séries de Volterra : applications aux systèmes sonores

Abstract : This thesis addresses the identification of nonlinear systems that can be represented with Volterra series, and its application to audio systems. Volterra series give an input-output representation, approximated to within a given error, of any time-invariant continuous nonlinear system with fading memory. Technically, they correspond to a series expansion sorted by homogeneity order with respect to the input: each homogeneous term is characterized by a convolutive kernel whose set provides a "complete signature" of the modelled system. The works presented are based on the development of a preliminary step that consists in separating the series' terms to improve Volterra kernels' identification. Compared to existing homogeneous order separation methods, which are based on amplitude relationships between test signals, the approach chosen in this thesis is to exploit phase relationships between signals to obtain a robust method. This is first obtained in the theoretical case of complex excitation signals. From this idea, several methods suited to the use of real signals are developed. This leads to define new signals categories that describes the output of a Volterra series, sorting nonlinear contributions according to their phase properties. The proposed separation methods are applied and tested on a guitar pedal effect. Then, specific identification methods for the new types of signals are presented Finally, a method for estimating the parameters of a polynomial nonlinear state-space representation is developed. This is applied to an electrodynamic loudspeaker whose nonlinear characteristics are studied.
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Submitted on : Wednesday, November 20, 2019 - 4:14:41 PM
Last modification on : Tuesday, May 26, 2020 - 3:33:32 AM


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  • HAL Id : tel-02372870, version 1


Damien Bouvier. Identification de systèmes non linéaires représentés en séries de Volterra : applications aux systèmes sonores. Son [cs.SD]. Sorbonne Université, 2018. Français. ⟨NNT : 2018SORUS162⟩. ⟨tel-02372870⟩



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