Exploiting nonlinearity in adaptive signal processing

Authors:
Phebe Vayanos;Mo Chen;Beth Jelfs;Danilo P. Mandic
Affiliations:
Department of Electrical and Electronic Engineering, Imperial College London, London, UK;Department of Electrical and Electronic Engineering, Imperial College London, London, UK;Department of Electrical and Electronic Engineering, Imperial College London, London, UK;Department of Electrical and Electronic Engineering, Imperial College London, London, UK
Venue:
NOLISP'07 Proceedings of the 2007 international conference on Advances in nonlinear speech processing
Year:
2007

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Abstract

Quantitative performance criteria for the analysis of machine learning architectures and algorithms have been long established. However, the qualitative performance criteria, e.g., nonlinearity assessment, are still emerging. To that end, we employ some recent developments in signal characterisation and derive criteria for the assessment of the changes in the nature of the processed signal. In addition, we also propose a novel online method for tracking the system nonlinearity. A comprehensive set of simulations in both the linear and nonlinear settings and their combination supports the analysis.