Consistent independent low-rank matrix analysis for determined blind source separation

Algorithms for Independent Low-Rank Matrix Analysis

This document summarizes an algorithm for independent low-rank matrix analysis, which was proposed as determined rank-1 multichannel nonnegative matrix factorization in the following published papers: Daichi Kitamura, Nobutaka Ono, Hiroshi Sawada, Hirokazu Kameoka, and Hiroshi Saruwatari, “Efficient multichannel nonnegative matrix factorization exploiting rank-1 spatial model,” Proceedings of I...

Independent component analysis and blind source separation

Independent Component Analysis and Blind Source Separation

Blind source separation and independent component analysis

This special issue of Neurocomputing contains a selection of eleven papers about blind source separation and independent component analysis. The papers represent improved and extended versions of contributed papers presented at the ICA 2004 meeting held in Granada, Spain. A total of 15 manuscripts have been suggested for the special issue by the referees of the contributions submitted to the co...

Sparse Kernel Independent Component Analysis for Blind Source Separation

We address the problem of Blind Source Separation (BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis (ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to est...