Detection of nonlinear distortion in audio signals

Abstract

Nonlinear distortion of a signal passing through a system may be caused by a number of factors. One of those factors, a limiter like transfer function, is considered. The nonlinear distortion causes a change in the probability density function (PDF) of the signal. The PDF of the signal can be characterized by the coefficients of a fifth-order polynomial fitted to the PDF curve. The coefficients are used as a vector input to an artificial neural network trained to classify the vector as belonging to a distorted or undistorted audio signal. Results show that the artificial neural network is able to classify signals, with PDFs indicating the presence of significant high amplitude components, into distorted or undistorted. A low amplitude signal will not be distorted during its passage through a nonlinear system and therefore the output will be classified as "not distorted". This gives rise to, what seem to be, errors in the classification of signals. However, the technique developed identifies distortion in the signal and not in the system through which the signal has passed.