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Double Sparsity: Towards Blind Estimation of Multiple Channels
"... Abstract. We propose a framework for blind multiple filter estimation from convolutive mixtures, exploiting the timedomain sparsity of the mixing filters and the disjointness of the sources in the timefrequency domain. The proposed framework includes two steps: (a) a clustering step, to determine ..."
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Abstract. We propose a framework for blind multiple filter estimation from convolutive mixtures, exploiting the timedomain sparsity of the mixing filters and the disjointness of the sources in the timefrequency domain. The proposed framework includes two steps: (a) a clustering step, to determine the frequencies where each source is active alone; (b) a filter estimation step, to recover the filter associated to each source from the corresponding incomplete frequency information. We show how to solve the filter estimation step (b) using convex programming, and we explore numerically the factors that drive its performance. Step (a) remains challenging, and we discuss possible strategies that will be studied in future work. Key words: blind filter estimation, sparsity, convex optimisation 1
Blind Separation of QuasiStationary Sources: Exploiting Convex Geometry in Covariance Domain
, 2015
"... This paper revisits blind source separation of instantaneously mixed quasistationary sources (BSSQSS), motivated by the observation that in certain applications (e.g., speech) there exist time frames during which only one source is active, or locally dominant. Combined with nonnegativity of sourc ..."
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This paper revisits blind source separation of instantaneously mixed quasistationary sources (BSSQSS), motivated by the observation that in certain applications (e.g., speech) there exist time frames during which only one source is active, or locally dominant. Combined with nonnegativity of source powers, this endows the problem with a nice convex geometry that enables elegant and efficient BSS solutions. Local dominance is tantamount to the socalled pure pixel/separability assumption in hyperspectral unmixing/nonnegative matrix factorization, respectively. Building on this link, a very simple algorithm called successive projection algorithm (SPA) is considered for estimating the mixing system in closed form. To complement SPA in the specific BSSQSS context, an algebraic preprocessing procedure is proposed to suppress shortterm source crosscorrelation interference. The proposed procedure is simple, effective, and supported by theoretical analysis. Solutions based on volume minimization (VolMin) are also considered. By theoretical analysis, it is shown that VolMin guarantees perfect mixing system identifiability under an assumption more relaxed than (exact) local dominance—which means wider applicability in practice. Exploiting the specific structure of BSSQSS, a fast VolMin algorithm is proposed for the overdetermined case. Careful simulations using real speech sources showcase the simplicity, efficiency, and accuracy of the proposed algorithms.
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 ..."
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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 of the corresponding transformations. When the sources are invariant against translations (spatial displacements or time shifts) the resulting model is called an anechoic mixing model. We present a new algorithmic framework for the solution of anechoic problems in arbitrary dimensions. This framework is derived from stochastic timefrequency analysis in general, and the marginal properties of the WignerVille spectrum in particular. The method reduces the general anechoic problem to a set of anechoic problems with nonnegativity constraints and a phase retrieval problem. The first type of subproblem can be solved by existing algorithms, for example by an appropriate modification of nonnegative matrix factorization (NMF). The second subproblem is solved by established phase retrieval methods. We discuss and compare implementations of this new algorithmic framework for several example problems with synthetic and realworld data, including music streams, natural 2D images, human motion trajectories and twodimensional shapes.
HYPERDEMIX: BLIND SOURCE SEPARATION OF HYPERSPECTRAL IMAGES USING LOCAL ML ESTIMATES
"... We propose a new method to unmix hyperspectral images. Our method exploits the structure of the material abundance maps by assuming that in some regions of the spatial dimension, only one material is present. Such regions provide a local estimate of the endmember spectrum of the corresponding materi ..."
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We propose a new method to unmix hyperspectral images. Our method exploits the structure of the material abundance maps by assuming that in some regions of the spatial dimension, only one material is present. Such regions provide a local estimate of the endmember spectrum of the corresponding material. Our main contribution is a new clustering algorithm called HyperDEMIX to estimate the endmember spectrum of each material based on such local estimates. The abundance map of each material is then recovered with a binary masking technique. Experimental results over noisy hyperspectral images show the effectiveness of the proposed approach. Index Terms — Blind source separation, hyperspectral images
MULTISOURCE TDOA ESTIMATION USING SNRBASED ANGULAR SPECTRA
, 2012
"... HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte p ..."
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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Département Traitement du Signal et des Images
"... Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation Factorisation en matrices à coefficients positifs de données multicanal convolutives pour la séparation de sources audio ..."
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Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation Factorisation en matrices à coefficients positifs de données multicanal convolutives pour la séparation de sources audio
Thème COG
"... apport de recherche ISSN 02496399 ISRN INRIA/RR6593FR+ENGinria00305435, version 2 4 Aug 2008A robust method to count, locate and separate audio sources in a multichannel underdetermined mixture ..."
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apport de recherche ISSN 02496399 ISRN INRIA/RR6593FR+ENGinria00305435, version 2 4 Aug 2008A robust method to count, locate and separate audio sources in a multichannel underdetermined mixture
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"... Doubly sparse models for multiple filter estimation in sparse echoic environments ..."
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Doubly sparse models for multiple filter estimation in sparse echoic environments
Open Access
"... TWEAK/Fn14 pathway modulates properties of a human microvascular endothelial cell model of blood brain barrier ..."
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TWEAK/Fn14 pathway modulates properties of a human microvascular endothelial cell model of blood brain barrier