Blind source separation using hellinger divergence and copulas

Robust Blind Source Separation by Beta Divergence

Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to ou...

Beta Divergence for Clustering in Monaural Blind Source Separation

General purpose audio blind source separation algorithms have to deal with a large dynamic range for the different sources to be separated. In our algorithm the mixture is separated into single notes. These notes are clustered to construct the melodies played by the active sources. The non-negative matrix factorization (NMF) leads to good results in clustering the notes according to spectral fe...

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