69,134 research outputs found
New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources
Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimization of contrast function. More precisely, we design a novel “reference-based” contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg.1 and Alg.2) to maximize it. Simulations confirm the convergence of our method to a separating solution, which is also analyzed in theory. We also validate the theoretic complexity analysis that Alg.2 has a much lower computational cost than Alg.1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications
Component separation methods for the Planck mission
The Planck satellite will map the full sky at nine frequencies from 30 to 857
GHz. The CMB intensity and polarization that are its prime targets are
contaminated by foreground emission. The goal of this paper is to compare
proposed methods for separating CMB from foregrounds based on their different
spectral and spatial characteristics, and to separate the foregrounds into
components of different physical origin. A component separation challenge has
been organized, based on a set of realistically complex simulations of sky
emission. Several methods including those based on internal template
subtraction, maximum entropy method, parametric method, spatial and harmonic
cross correlation methods, and independent component analysis have been tested.
Different methods proved to be effective in cleaning the CMB maps from
foreground contamination, in reconstructing maps of diffuse Galactic emissions,
and in detecting point sources and thermal Sunyaev-Zeldovich signals. The power
spectrum of the residuals is, on the largest scales, four orders of magnitude
lower than that of the input Galaxy power spectrum at the foreground minimum.
The CMB power spectrum was accurately recovered up to the sixth acoustic peak.
The point source detection limit reaches 100 mJy, and about 2300 clusters are
detected via the thermal SZ effect on two thirds of the sky. We have found that
no single method performs best for all scientific objectives. We foresee that
the final component separation pipeline for Planck will involve a combination
of methods and iterations between processing steps targeted at different
objectives such as diffuse component separation, spectral estimation and
compact source extraction.Comment: Matches version accepted by A&A. A version with high resolution
figures is available at http://people.sissa.it/~leach/compsepcomp.pd
Jointly Tracking and Separating Speech Sources Using Multiple Features and the generalized labeled multi-Bernoulli Framework
This paper proposes a novel joint multi-speaker tracking-and-separation
method based on the generalized labeled multi-Bernoulli (GLMB) multi-target
tracking filter, using sound mixtures recorded by microphones. Standard
multi-speaker tracking algorithms usually only track speaker locations, and
ambiguity occurs when speakers are spatially close. The proposed multi-feature
GLMB tracking filter treats the set of vectors of associated speaker features
(location, pitch and sound) as the multi-target multi-feature observation,
characterizes transitioning features with corresponding transition models and
overall likelihood function, thus jointly tracks and separates each
multi-feature speaker, and addresses the spatial ambiguity problem. Numerical
evaluation verifies that the proposed method can correctly track locations of
multiple speakers and meanwhile separate speech signals
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