Blind source separation using Renyi's α-marginal entropies

Blind source separation using Renyi's -marginal entropies

We have recently suggested the minimization of a nonparametric estimator of Renyi’s mutual information as a criterion for blind source separation. Using a two-stage topology, consisting of spatial whitening and a series of Givens rotations, the cost function reduces to the sum of marginal entropies, just like in the Shannon’s entropy case. Since we use a Parzen window density estimator and elim...

A Robust Watermarking using Blind Source Separation

In this paper, we present a robust and secure algorithm for watermarking, the watermark is first transformed into the frequency domain using the discrete wavelet transform (DWT). Then the entire DWT coefficient except the LL (Band) discarded, these coefficients are permuted and encrypted by specific mixing. The encrypted coefficients are inserted into the most significant spectral components of...

Nonlinear Blind Source Separation Using Ensemble Learning

Blind source separation (BSS) and the closely related Independent Component Analysis (ICA) have recently drawn a lot of attention in unsupervised neural learning and statistical signal processing 1, 2, 3]. In the basic linear case, ICA and BSS use the same data model x(t) = As(t) = m X i=1 s i (t)a i : (1) Here x(t) is the n-dimensional t:th data vector, s 1 (t); : : : ; s m (t) are the respect...

Novel blind source separation algorithms using cumulants

Anechoic Blind Source Separation Using Wigner Marginals

Blind source separation problems emerge in many applications, where signals can be modeled as superpositions of multiple sources. Many popular applications of blind source separation are based on linear instantaneous mixture models. If specific invariance properties are known about the sources, for example, translation or rotation invariance, the simple linear model can be extended by inclusion...