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Blind Source Separation Of Noisy Mixtures Using A Semi-Parametric Approach With Application To Heavy-Tailed Signals
2005
Zenodo
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INTRODUCTION Blind source separation is one of the most attractive research topics nowadays in the field of signal processing and its applications [7] . ...
The proposed method is applied for the blind separation of noisy linear instantaneous mixtures of possibly heavy-tailed sources. ...
doi:10.5281/zenodo.39400
fatcat:fqdkgtn5ojcaxojovj2uglew3e
Application of Blind Source Separation Algorithms and Ambient Vibration Testing to the Health Monitoring of Concrete Dams
2016
Mathematical Problems in Engineering
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In this work, using AVT data, a health monitoring method for concrete dams based on two different blind source separation (BSS) methods, that is, second-order blind identification (SOBI) and independent ...
The selected modes are then used to calculate modal features and are analysed using ICA to extract some independent components (ICs). ...
Recently, one area of research focused on OMA methods is the use of the blind source separation (BSS) technique to implement output-only modal identification. ...
doi:10.1155/2016/4280704
fatcat:6nokyigj4naxtopu4dhbsc4vbq
Independent Subspace Analysis on Innovations
[chapter]
2005
Lecture Notes in Computer Science
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Independent subspace analysis (ISA) that deals with multidimensional independent sources, is a generalization of independent component analysis (ICA). ...
The innovation process instead of the original mixtures has been proposed to solve ICA problems with temporal dependencies. Here we show that this strategy can be applied to ISA as well. ...
There is a broad range of applications for ICA, such as blind source separation and blind source deconvolution [3] , feature extraction [4] , denoising [5] . ...
doi:10.1007/11564096_71
fatcat:v6ccgastzzep3j2vz36p4rvnle
Underdetermined joint blind source separation with application to physiological data
2017
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Joint Blind Source Separation for Multiple Datasets (UJBSS-M). ...
Blind Source Separation (BSS) methods have been attracting increasing attention for their promising applications in signal processing. ...
Short-Time Fourier transform TF Time-Frequency UBSS Underdetermined Blind Source Separation UJBSS Underdetermined Joint Blind Source Separation UJBSS-2 Underdetermined Joint Blind Source Separation for ...
doi:10.14288/1.0355539
fatcat:az4qhkcvqfhn7i6ktu5meiadie
Cross-Entropy Optimization for Independent Process Analysis
[chapter]
2006
Lecture Notes in Computer Science
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We treat the problem of searching for hidden multi-dimensional independent auto-regressive processes. First, we transform the problem to Independent Subspace Analysis (ISA). ...
We show that under certain conditions, ISA is equivalent to a combinatorial optimization problem. For the solution of this optimization we apply the cross-entropy method. ...
There are important applications in this field, such as blind source separation, blind source deconvolution, feature extraction and denoising. ...
doi:10.1007/11679363_113
fatcat:qihe3rxpafa5vi5dv4bxilh2d4
Independent component analysis for brain fMRI does not select for independence
2009
Proceedings of the National Academy of Sciences of the United States of America
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The first blind source separation in fMRI via ICA used InfoMax (1); other ICA algorithms for fMRI followed, such as FastICA (2, 5). ...
In particular, we conclude that independence is not the right mathematical framework for blind source separation in fMRI; representations in which the fMRI signal is sparse are more promising. ...
Nowak for their comments. ...
doi:10.1073/pnas.0903525106
pmid:19556548
pmcid:PMC2705604
fatcat:jrmzb7iuv5eibgtznau5mhbbny
Auto-regressive independent process analysis without combinatorial efforts
2009
Pattern Analysis and Applications
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The so-called separation theorem simplifies the ISA task considerably: the theorem enables one to reduce the task to one-dimensional Blind Source Separation (BSS) task followed by the grouping of the coordinates ...
We treat the problem of searching for hidden multi-dimensional independent auto-regressive processes (Auto-Regressive Independent Process Analysis, AR-IPA). ...
Acknowledgements This research has been supported by the EC NEST 'Perceptual Consciousness: Explication and Testing' grant under contract 043261. ...
doi:10.1007/s10044-009-0174-x
fatcat:qxra5etvejbxnlzqsfcvcplwcq
Adaptive independent component analysis of multichannel electrogastrograms
2001
Medical Engineering and Physics
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Simulation results show that our algorithm is able to separate a wide range of source signals, including mixtures of Gaussian sources. ...
In this paper, a novel blind signal separation method with a flexible non-linearity is introduced and applied to extract the gastric slow wave from multichannel EGGs. ...
Acknowledgements The author would like to thank Prof. Z. Y. Lin for his technical assistance and for the data used in this paper. ...
doi:10.1016/s1350-4533(01)00019-4
pmid:11413061
fatcat:xjx7y27iujgttdk4apesre25l4
A class of neural networks for independent component analysis
1997
IEEE Transactions on Neural Networks
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In this application only the source signals which correspond to the coefficients of the ICA expansion are of interest. ...
Index Terms-Blind source separation, independent component analysis, neural networks, principal component analysis, signal processing, unsupervised learning. ...
ACKNOWLEDGMENT The authors are grateful to the reviewers for their detailed and useful comments. ...
doi:10.1109/72.572090
pmid:18255654
fatcat:7ycqgkg5yvdhflruafe75wfaay
Time Series Forecasting Using Independent Component Analysis
2009
Zenodo
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The idea of this approach is to do the forecasting in the space of independent components (sources), and then to transform back the results to the original time series space. ...
The forecasting can be done separately and with a different method for each component, depending on its time structure. ...
ICA is closely related to the method Blind Source Separation (BSS) problem. A "source" means here an original component, i.e. independent component. ...
doi:10.5281/zenodo.1074779
fatcat:x2fv4zgetrc25n3zv7okbjequu
A unifying information-theoretic framework for independent component analysis
2000
Computers and Mathematics with Applications
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We show that different theories recently proposed for independent component analysis (ICA) lead to the same iterative learning algorithm for blind separation of mixed independent sources. ...
Finally, we discuss convergence and stability as well as future research issues in blind source separation. (~) ...
the blind separation of sources. ...
doi:10.1016/s0898-1221(00)00101-2
fatcat:rrjspm47sfhxpms3bi7k6o24qm
Generalized sparse signal mixing model and application to noisy blind source separation
2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
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The solution obtained is applicable to an arbitrary number of microphones and sources, but works best when the number of sources simultaneously active at any time frequency point is a small fraction of ...
This is shown to result from an assumption of sparseness of the sources themselves, and allows us to solve the maximum likelihood formulation of the non-instantaneous acoustic mixing source estimation ...
First we discuss the form of our sparsity assumption, then we present its application to blind source separation of noisy real-world audio signals. ...
doi:10.1109/icassp.2004.1326685
dblp:conf/icassp/RoscaBB04
fatcat:ggy252pnqrfoblkp6xbuwzhfk4
In-vehicle speaker recognition using independent vector analysis
2012
2012 15th International IEEE Conference on Intelligent Transportation Systems
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Independent component analysisbased blind source separation algorithms have attracted attentions in recent years in the application of speech separation and enhancement. ...
Compared to the traditional beamforming technique, the blind source separation method may typically require less number of microphones and perform better under reverberant environment. ...
The authors would also like to thank the reviewers for their insightful comments. ...
doi:10.1109/itsc.2012.6338907
dblp:conf/itsc/YamadaTT12
fatcat:qka5fiojmbc7fk5bbmdyoqycjy
Recursive Generalized Eigendecomposition for Independent Component Analysis
[chapter]
2006
Lecture Notes in Computer Science
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Independent component analysis is an important statistical tool in machine learning, pattern recognition, and signal processing. Most of these applications require on-line learning algorithms. ...
Current on-line ICA algorithms use the stochastic gradient concept, drawbacks of which include difficulties in selecting the step size and generating suboptimal estimates. ...
Introduction Independent component analysis (ICA) has now established itself as an essential statistical tool in signal processing and machine learning, both as a solution to problems (such as blind source ...
doi:10.1007/11679363_25
fatcat:7u3iiliblrcrtbtct5vwt6d6yy
A comparative study of culture-independent, library-independent genotypic methods of fecal source tracking
2003
Journal of Water and Health
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Culture independent methods show considerable promise; further research is needed to develop markers for additional fecal sources and to understand the correlation of these source-tracking indicators to ...
Culture-independent fecal source tracking methods have many potential advantages over library-dependent, isolate-culture methods, but they have been subjected to limited testing. ...
We report on results from these analyses, plus additional analyses carried out after the end of the blind testing. ...
doi:10.2166/wh.2003.0020
pmid:15382723
fatcat:qejidiiz7zdgtcmxqdvmyseuiy
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