Constrained independent vector extraction of quasi-periodic signals from multiple data sets

Abstract In this paper, we consider a problem to extract independent source component vector (SCV) formed by quasi-periodic signals from instantaneous mixtures in multiple data sets. We propose a method, termed as constrained independent vector extraction (CIVE), to uniquely determine the target quasi-periodic SCV. Specifically, the negentropy is taken to enforce the independence of the target SCV from the others, while the mutual information is used to determine the correlation of sources within the target SCV. A quasi-periodic constraint is further combined in the cost function to ensure the quasi-periodicity of the SCV. The demixing vectors of the target SCV are solved as a constrained optimization problem by the Lagrange multiplier method. The CIVE method is designed to work under diverse probability distributions for the mixed signals. In the experiments, the CIVE method is verified with both simulated and semi-simulated data. The comparison results with other methods indicate the effectiveness, applicability and stability of the proposed method for extracting quasi-periodic SCVs.