Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Natural gradient works efficiently in learning
Neural Computation
Semiparametric model and superefficiency in blind deconvolution
Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
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In this paper, we study the blind source separation problem of temporally correlated signals via exploring both the temporal structure and high-order statistics of source signals. First, we formulate the problem as independent residual analysis and present a simple cost function. Efficient learning algorithm is developed for the demixing matrix and the corresponding stability analysis is also provided. The formulation provides much more flexibility for us to identify learning algorithms with good learning performance and stability. Furthermore, the approach unifies the conventional high-order statistical method and the second-order statistical method. From stability analysis, we infer that if the temporal filters of sources are mutually different, the second order statistical algorithm will be sufficient to separate the sources from their linear mixtures.