Noisy component extraction (NoiCE)

Authors:
Wai Yie Leong;Danilo P. Mandic
Affiliations:
Singapore Institute of Manufacturing Technology, Singapore, Singapore and Department of Electronics and Electrical Engineering, Imperial College London, London, UK;Department of Electronics and Electrical Engineering, Imperial College London, London, UK
Venue:
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Year:
2010

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Abstract

To achieve efficient blind source extraction (BSE) from noisy mixtures, we propose a noisy component extraction (NoiCE) algorithm that combines standard BSE and a cascaded nonlinear adaptive estimator. There are no assumptions of statistical independence, and also as a byproduct of BSE after deflation, we may also obtain asymptotic identification of the a priori unknown observation noise sources. By yielding an asymptotically efficient estimator in the presence of an unknown observation noise, the proposed algorithm may also be viewed as a robust approach to NoiCE. Simulations on both synthetic and real-world data confirm the validity of the proposed algorithm in noisy mixing environments.