General approach to blind source separation

This paper identifies and studies two major issues in the blind source separation problem: separability and separation principles. We show that separability is an intrinsic property of the measured signals and can be described by the concept of m-row decomposability introduced in this paper; we also show that separation principles can be developed by using the structure characterization theory of random variables. In particular, we show that these principles can be derived concisely and intuitively by applying the Darmois-Skitovich theorem, which is well known in statistical inference theory and psychology. Some new insights are gained for designing blind source separation filters.