  Blind source separation (BSS) is one of the most important and established
research topics in signal processing and many algorithms have been proposed
based on different statistical properties of the source signals. For
second-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 noise
and 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 the
variance information of noise is not required. An adaptive blind source
extraction algorithm is derived based on this idea and a further extension is
proposed by employing a dual-linear predictor structure for blind source
extraction (BSE).
