Frequency-Domain Blind Source Separation with Permutation Control

This paper explores the problem of frequency-domain Blind Source Separation (BSS) of convolutive mixtures. The main difficulties of this approach lie in the so called permutation and amplitude problems. In order to solve the permutation ambiguity, a new hybrid approach is proposed, in which the Independent Component Analysis (ICA) processes across all frequency bins are concatenated and each of them is embedded with a permutation control unit. In each frequency bin, when the separation matrix is obtained by the ICA process, the control unit detects the possible permutation and aligns the matrix only if the permutation is confirmed. Then the final value of separation matrix is used to initialize the ICA iterations in the next frequency bin. The amplitude problem is addressed by utilizing the elements in estimated mixing matrix. The method is compared with conventional frequency-domain BSS approaches and the experimental results demonstrate superior performances of the proposed method

An adaptive stereo basis method for convolutive blind audio source separation

NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [71, 10-12, June 2008] DOI:neucom.2007.08.02

SZ and CMB reconstruction using Generalized Morphological Component Analysis

In the last decade, the study of cosmic microwave background (CMB) data has become one of the most powerful tools to study and understand the Universe. More precisely, measuring the CMB power spectrum leads to the estimation of most cosmological parameters. Nevertheless, accessing such precious physical information requires extracting several different astrophysical components from the data. Recovering those astrophysical sources (CMB, Sunyaev-Zel'dovich clusters, galactic dust) thus amounts to a component separation problem which has already led to an intense activity in the field of CMB studies. In this paper, we introduce a new sparsity-based component separation method coined Generalized Morphological Component Analysis (GMCA). The GMCA approach is formulated in a Bayesian maximum a posteriori (MAP) framework. Numerical results show that this new source recovery technique performs well compared to state-of-the-art component separation methods already applied to CMB data.Comment: 11 pages - Statistical Methodology - Special Issue on Astrostatistics - in pres