Results 11 to 20 of about 451,315 (310)
Blind source separation using temporal predictability [PDF]
Neural Computation, 2001 A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the ...
Stone, J.V.
core +6 more sources
Blind Source Separation: the Sparsity Revolution [PDF]
, 2008 International audienceOver the last few years, the development of multi-channel sensors motivated interest in methods for the coherent processing of multivariate data.
Bobin, J. +3 more
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Spatial blind source separation [PDF]
Biometrika, 2020 SummaryRecently a blind source separation model was suggested for spatial data, along with an estimator based on the simultaneous diagonalization of two scatter matrices. The asymptotic properties of this estimator are derived here, and a new estimator based on the joint diagonalization of more than two scatter matrices is proposed.
Bachoc, François +5 more
openaire +6 more sources
Blind Source Separation for Compositional Time Series. [PDF]
Math Geosci, 2021 AbstractMany geological phenomena are regularly measured over time to follow developments and changes. For many of these phenomena, the absolute values are not of interest, but rather the relative information, which means that the data are compositional time series.
Nordhausen K, Fischer G, Filzmoser P.
europepmc +5 more sources
Multiscale blind source separation [PDF]
The Annals of Statistics, 2018 We provide a new methodology for statistical recovery of single linear mixtures of piecewise constant signals (sources) with unknown mixing weights and change points in a multiscale fashion. We show exact recovery within an $ε$-neighborhood of the mixture when the sources take only values in a known finite alphabet.
Behr, Merle, Holmes, Chris, Munk, Axel
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, 2014 Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). This edited book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications.
Xianchuan Yu, Dan Hu, Jindong Xu
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JCCM: Joint conformer and CNN model for overlapping radio signals recognition
Electronics Letters, 2023 Overlapping radio signals recognition is attracting more attention as the development and ubiquitous application of radio technologies. The traditional blind signal separation (BSS) method is mostly not effective when both radio propagation effects and ...
Junbin Liang +5 more
doaj +1 more source
Neural Full-Rank Spatial Covariance Analysis for Blind Source Separation
IEEE Signal Processing Letters, 2021 This paper describes aneural blind source separation (BSS) method based on amortized variational inference (AVI) of a non-linear generative model of mixture signals.
Yoshiaki Bando +5 more
semanticscholar +1 more source
Underdetermined Blind Source Separation Algorithm for Speech Signal Based on DSKSVD Dictionary Learning [PDF]
Jisuanji gongcheng, 2018 In order to overcome the shortcoming that the traditional learning algorithm training has limited dictionary size and large amount of computation,the algorithm of underdetermined blind source separation for speech signal based on the dictionary learning ...
LI Hu,XU Yan
doaj +1 more source
Nonlinear blind source separation for sparse sources [PDF]
2016 24th European Signal Processing Conference (EUSIPCO), 2016 Blind Source Separation (BSS) is the problem of separating signals which are mixed through an unknown function from a number of observations, without any information about the mixing model. Although it has been mathematically proven that the separation can be done when the mixture is linear, there is not any proof for the separability of nonlinearly ...
Ehsandoust, Bahram +3 more
openaire +2 more sources