”Gaussianization” Method for identification of memoryless nonlinear audio systems

Identification and compensation purposes of nonlinear systems are of interest for many audio processing applications. The analysis of systems under test must be done through realistic audio inputs in order to capture different aspects of the nonlinearity. However, the Gaussianity of the tested signal, is a desirable factor because it guarantees easy implementation and good performances for the nonlinearity identification process. In this paper, we show at a first stage, the importance of input Gaussianity for the identification of memoryless nonlinear systems. At a second stage, we propose an algorithm that makes the speech signals Gaussian. The proposed “Gaussianization” algorithm is based on the embedding of an imperceptible signal in the speech signal, to force it to be Gaussian. As expected, the performances of the optimal identification of a polynomial nonlinearity are much better with the Gaussianized input than with the original one. Moreover, these performances exhibit a robustness similar to the Gaussian input case.