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Blind source separation (BSS) is one of the most important and establishedresearch topics in signal processing and many algorithms have been proposedbased on different statistical properties of the source signals. Forsecond-order statistics (SOS) based methods, canonical correlation analysis(CCA) has been proved to be an effective solution to the problem. In this work,the CCA approach is generalized to accommodate the case with added white noiseand it is then applied to the BSS problem for noisy mixtures. In this approach,the noise component is assumed to be spatially and temporally white, but thevariance information of noise is not required. An adaptive blind sourceextraction algorithm is derived based on this idea and a further extension isproposed by employing a dual-linear predictor structure for blind sourceextraction (BSE).

Generalized Canonical Correlation Analysis and Its Application to Blind Source Separation Based on a Dual-Linear Predictor Structure