  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 metric comparison performance with FastICA,
RobustICA, convex ICA (C ICA), and other leading existing algorithms.
