Convex Cauchy Schwarz Independent Component Analysis for Blind Source Separation

—We present a new high-performance Convex Cauchy– Schwarz Divergence (CCS-DIV) measure for Independent Component Analysis (ICA) and Blind Source Separation (BSS). The CCS-DIV measure is developed by integrating convex functions into the Cauchy–Schwarz inequality. By including a convexity quality parameter, the measure has a broad control range of its convexity curvature. With this measure, a new CCS–ICA algorithm is structured and a non-parametric form is developed incorporating the Parzen window-based distribution. Furthermore, pairwise iterative schemes are employed to tackle the high dimensional problem in BSS. We present two schemes of pairwise non-parametric ICA algorithms, one is based on gradient decent and the second on the Jacobi Iterative method. Several case-study scenarios are carried out on noise-free and noisy mixtures of speech and music signals. Finally, the superiority of the proposed CCS–ICA algorithm is demonstrated in " case-study " metric-comparison performance with FastICA, RobustICA, convex ICA (C-ICA), and other leading existing algorithms.