Higher-order time frequency-based blind source separation technique

This letter considers the separation and estimation of independent sources from their instantaneous linear mixed observed data. Here, unknown source s ignals are estimated from their unknown linear mixtures using the strong as sumption that the sources are mutually independent. In practice, separation can be achieved by using suitable second- or higher-order statistics. The authors propose a novel source separation technique exploiting fourth-order time frequency distributions. A computationally feasible implementation is presented based on joint diagonalization of the matrices of the principal slices of time-multifrequency domain of support of the cumulant-based Wigne r trispectrums. A numerical example demonstrates the effectiveness of the p roposed approach.