Blind signal separation using oriented PCA neural models

Oriented PCA (OPCA) is a (second order) extension of standard principal component analysis aiming at maximizing the power ratio of a pair of signals. It is shown that OPCA, preceded by almost arbitrary temporal filtering, can be used for blindly separating temporally colored signals from their linear instantaneous mixtures. The advantage over other second order techniques is the lack of the prewhitening (or sphereing) step. Although the design of the general optimal temporal pre-filter is an open problem, we show that the filters [1, ±1] are the optimal ones for the special two-tap case. Neural OPCA models proposed earlier are used in simulations to separate a number of artificial sources demonstrating the validity of the method.