Functional link expansions for nonlinear modeling of audio and speech signals

Nonlinear distortions pose a serious problem for the quality preservation of audio and speech signals. To address this problem, such signals are processed by nonlinear models. Functional link adaptive filter (FLAF) is a linear-in-the-parameter nonlinear model, whose nonlinear transformation of the input is characterized by a basis function expansion, satisfying the universal approximation properties. Since the expansion type affects the nonlinear modeling according to the nature of the input signal, in this paper we investigate the FLAF modeling performance involving the most popular functional expansions when audio and speech signals are processed. A comprehensive analysis is conducted to provide the best suitable solution for the processing of nonlinear signals. Experimental results are assessed also in terms of signal quality and intelligibility.