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Independent Component Analysis and Blind Signal Separation

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In many situations found both in Nature and in human-built systems, a set of mixed signalsisobserved(frequentlyalsowithnoise),andi... Weiterlesen
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In many situations found both in Nature and in human-built systems, a set of mixed signalsisobserved(frequentlyalsowithnoise),anditisofgreatscienti?candtech- logicalrelevanceto beableto isolateor separatethemso thattheinformationin each ofthesignalscanbeutilized. Blind source separation (BSS) research is one of the more interesting emerging ?eldsnowadaysinthe?eldofsignalprocessing.Itdealswiththealgorithmsthatallow therecoveryoftheoriginalsourcesfromasetofmixturesonly.Theadjective blind is applied becausethe purposeis to estimate the originalsourceswithoutany a priori knowledgeabouteitherthesourcesorthemixingsystem.Mostofthemodelsemployed in BSS assume the hypothesisabout the independenceof the original sources. Under this hypothesis,a BSS problemcan be consideredas a particularcase of independent componentanalysis(ICA),alineartransformationtechniquethat,startingfromam- tivariate representation of the data, minimizes the statistical dependence between the componentsoftherepresentation.Itcan beclaimed thatmostoftheadvancesin ICA havebeenmotivatedbythesearchforsolutionstotheBSSproblemand,theotherway around,advancesinICAhavebeenimmediatelyappliedtoBSS. ICA and BSS algorithms start from a mixture model, whose parameters are - timated from the observed mixtures. Separation is achieved by applying the inverse mixturemodelto theobservedsignals(separatingorunmixingmodel).Mixturem- els usually fall into three broad categories: instantaneous linear models, convolutive modelsandnonlinearmodels,the?rstonebeingthesimplestbut,in general,notnear realisticapplications.Thedevelopmentandtestofthealgorithmscanbeaccomplished throughsyntheticdataorwithreal-worlddata.Obviously,themostimportantaim(and mostdif?cult)istheseparationofreal-worldmixtures.BSSandICAhavestrongre- tionsalso,apartfromsignalprocessing,withother?eldssuchasstatisticsandarti?cial neuralnetworks. As long as we can ?nd a system that emits signals propagated through a mean, andthosesignalsarereceivedbyasetofsensorsandthereisaninterestinrecovering the originalsources,we have a potential?eld ofapplication forBSS and ICA. Inside thatwiderangeofapplicationswecan?nd,forinstance:noisereductionapplications, biomedicalapplications,audiosystems,telecommunications,andmanyothers. This volume comes out just 20 years after the ?rst contributionsin ICA and BSS 1 appeared . Thereinafter,the numberof research groupsworking in ICA and BSS has been constantly growing, so that nowadays we can estimate that far more than 100 groupsareresearchinginthese?elds. Asproofoftherecognitionamongthescienti?ccommunityofICAandBSSdev- opmentstherehavebeennumerousspecialsessionsandspecialissuesinseveralwell- 1 J.Herault, B.Ans, Circuits neuronaux à synapses modi?ables: décodage de messages c- posites para apprentissage non supervise , C.R. de l Académie des Sciences, vol. 299, no. III-13,pp.525 528,1984.

Includes supplementary material: sn.pub/extras


This book constitutes the refereed proceedings of the 5th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2004, held in Granada, Spain, in September 2004.

The 156 revised papers presented were carefully reviewed and selected from 203 submissions. The papers are organized in topical sections on theory and foundations, linear models, covolutive models, nonlinear models, speech processing applications, image processing applications, biomedical applications, and other applications.

Theory and Fundamentals.- A FastICA Algorithm for Non-negative Independent Component Analysis.- Blind Source Separation by Adaptive Estimation of Score Function Difference.- Exploiting Spatiotemporal Information for Blind Atrial Activity Extraction in Atrial Arrhythmias.- Gaussianizing Transformations for ICA.- New Eigensystem-Based Method for Blind Source Separation.- Optimization Issues in Noisy Gaussian ICA.- Optimization Using Fourier Expansion over a Geodesic for Non-negative ICA.- The Minimum Support Criterion for Blind Signal Extraction: A Limiting Case of the Strengthened Young's Inequality.- Accurate, Fast and Stable Denoising Source Separation Algorithms.- An Overview of BSS Techniques Based on Order Statistics: Formulation and Implementation Issues.- Analytical Solution of the Blind Source Separation Problem Using Derivatives.- Approximate Joint Diagonalization Using a Natural Gradient Approach.- BSS, Classification and Pixel Demixing.- Blind Identification of Complex Under-Determined Mixtures.- Blind Separation of Heavy-Tailed Signals Using Normalized Statistics.- Blind Source Separation of Linear Mixtures with Singular Matrices.- Closely Arranged Directional Microphone for Source Separation.- Estimating Functions for Blind Separation when Sources Have Variance-Dependencies.- Framework of Constrained Matrix Gradient Flows.- Identifiability, Subspace Selection and Noisy ICA.- Improving GRNNs in CAD Systems.- Fisher Information in Source Separation Problems.- Localization of P300 Sources in Schizophrenia Patients Using Constrained BSS.- On the Estimation of the Mixing Matrix for Underdetermined Blind Source Separation in an Arbitrary Number of Dimensions.- On the Minimum ?1-Norm Signal Recovery in Underdetermined Source Separation.- On the Strong Uniqueness of Highly Sparse Representations from Redundant Dictionaries.- Reliability of ICA Estimates with Mutual Information.- Robust ICA for Super-Gaussian Sources.- Robustness of Prewhitening Against Heavy-Tailed Sources.- Simultaneous Extraction of Signal Using Algorithms Based on the Nonstationarity.- Space-Time Variant Blind Source Separation with Additive Noise.- The Use of ICA in Speckle Noise.- Theoretical Method for Solving BSS-ICA Using SVM.- Wavelet De-noising for Blind Source Separation in Noisy Mixtures.- Linear Mixture Models.- A Gaussian Mixture Based Maximization of Mutual Information for Supervised Feature Extraction.- Blind Separation of Nonstationary Sources by Spectral Decorrelation.- Delayed AMUSE A Tool for Blind Source Separation and Denoising.- Dimensionality Reduction in ICA and Rank-(R 1,R 2,...,R N ) Reduction in Multilinear Algebra.- Linear Multilayer Independent Component Analysis Using Stochastic Gradient Algorithm.- Minimax Mutual Information Approach for ICA of Complex-Valued Linear Mixtures.- Signal Reconstruction in Sensor Arrays Using Temporal-Spatial Sparsity Regularization.- Underdetermined Source Separation with Structured Source Priors.- A Grassmann-Rayleigh Quotient Iteration for Dimensionality Reduction in ICA.- An Approach of Moment-Based Algorithm for Noisy ICA Models.- Geometrical ICA-Based Method for Blind Separation of Super-Gaussian Signals.- A Novel Method to Recover N Sources from N-1 Observations and Its Application to Digital Communications.- A Sufficient Condition for Separation of Deterministic Signals Based on Spatial Time-Frequency Representations.- Adaptive Robust Super-exponential Algorithms for Deflationary Blind Equalization of Instantaneous Mixtures.- Application of Gaussian Mixture Models for Blind Separation of Independent Sources.- Asymptotically Optimal Blind Separation of Parametric Gaussian Sources.- Bayesian Approach for Blind Separation of Underdetermined Mixtures of Sparse Sources.- Blind Source Separation Using the Block-Coordinate Relative Newton Method.- Hybridizing Genetic Algorithms with ICA in Higher Dimension.- ICA Using Kernel Entropy Estimation with NlogN Complexity.- Soft-LOST: EM on a Mixture of Oriented Lines.- Some Gradient Based Joint Diagonalization Methods for ICA.- Underdetermined Independent Component Analysis by Data Generation.- Convolutive Models.- Batch Mutually Referenced Separation Algorithm for MIMO Convolutive Mixtures.- Frequency Domain Blind Source Separation for Many Speech Signals.- ICA Model Applied to Multichannel Non-destructive Evaluation by Impact-Echo.- Monaural Source Separation Using Spectral Cues.- Multichannel Speech Separation Using Adaptive Parameterization of Source PDFs.- Non-negative Matrix Factor Deconvolution; Extraction of Multiple Sound Sources from Monophonic Inputs.- Optimal Sparse Representations for Blind Deconvolution of Images.- Separation of Convolutive Mixtures of Cyclostationary Sources: A Contrast Function Based Approach.- A Continuous Time Balanced Parametrization Approach to Multichannel Blind Deconvolution.- A Frequency-Domain Normalized Multichannel Blind Deconvolution Algorithm for Acoustical Signals.- A Novel Hybrid Approach to the Permutation Problem of Frequency Domain Blind Source Separation.- Application of Geometric Dependency Analysis to the Separation of Convolved Mixtures.- Blind Deconvolution of SISO Systems with Binary Source Based on Recursive Channel Shortening.- Blind Deconvolution Using the Relative Newton Method.- Blind Equalization Using Direct Channel Estimation.- Blind MIMO Identification Using the Second Characteristic Function.- Blind Signal Separation of Convolutive Mixtures: A Time-Domain Joint-Diagonalization Approach.- Characterization of the Sources in Convolutive Mixtures: A Cumulant-Based Approach.- CICAAR: Convolutive ICA with an Auto-regressive Inverse Model.- Detection by SNR Maximization: Application to the Blind Source Separation Problem.- Estimating the Number of Sources for Frequency-Domain Blind Source Separation.- Estimating the Number of Sources in a Noisy Convolutive Mixture Using BIC.- Evaluation of Multistage SIMO-Model-Based Blind Source Separation Combining Frequency-Domain ICA and Time-Domain ICA.- On Coefficient Delay in Natural Gradient Blind Deconvolution and Source Separation Algorithms.- On the FIR Inversion of an Acoustical Convolutive Mixing System: Properties and Limitations.- Overcomplete BSS for Convolutive Mixtures Based on Hierarchical Clustering.- Penalty Function Approach for Constrained Convolutive Blind Source Separation.- Permutation Alignment for Frequency Domain ICA Using Subspace Beamforming Methods.- QML Blind Deconvolution: Asymptotic Analysis.- Super-exponential Methods Incorporated with Higher-Order Correlations for Deflationary Blind Equalization of MIMO Linear Systems.- Nonlinear ICA and BSS.- Blind Maximum Likelihood Separation of a Linear-Quadratic Mixture.- Markovian Source Separation in Post-nonlinear Mixtures.- Non-linear ICA by Using Isometric Dimensionality Reduction.- Postnonlinear Overcomplete Blind Source Separation Using Sparse Sources.- Second-Order Blind Source Separation Based on Multi-dimensional Autocovariances.- Separating a Real-Life Nonlinear Mixture of Images.- Independent Slow Feature Analysis and Nonlinear Blind Source Separation.- Nonlinear PCA/ICA for the Structure from Motion Problem.- Plugging an Histogram-Based Contrast Function on a Genetic Algorithm for Solving PostNonLinear-BSS.- Post-nonlinear Independent Component Analysis by Variational Bayesian Learning.- Temporal Decorrelation as Preprocessing for Linear and Post-nonlinear ICA.- Tree-Dependent and Topographic Independent Component Analysis for fMRI Analysis.- Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method.- Speech Processing Applications.- A Geometric Approach for Separating Several Speech Signals.- A Novel Method for Permutation Correction in Frequency-Domain in Blind Separation of Speech Mixtures.- Convolutive Acoustic Mixtures Approximation to an Instantaneous Model Using a Stereo Boundary Microphone Configuration.- DOA Detection from HOS by FOD Beamforming and Joint-Process Estimation.- Nonlinear Postprocessing for Blind Speech Separation.- Real-Time Convolutive Blind Source Separation Based on a Broadband Approach.- A New Approach to the Permutation Problem in Frequency Domain Blind Source Separation.- Adaptive Cross-Channel Interference Cancellation on Blind Source Separation Outputs.- Application of the Mutual Information Minimization to Speaker Recognition / Verification Improvement.- Single Channel Speech Enhancement: MAP Estimation Using GGD Prior Under Blind Setup.- Stable and Low-Distortion Algorithm Based on Overdetermined Blind Separation for Convolutive Mixtures of Speech.- Two Channel, Block Adaptive Audio Separation Using the Cross Correlation of Time Frequency Information.- Underdetermined Blind Separation of Convolutive Mixtures of Speech with Directivity Pattern Based Mask and ICA.- Image Processing Applications.- A Digital Watermarking Technique Based on ICA Image Features.- A Model for Analyzing Dependencies Between Two ICA Features in Natural Images.- An Iterative Blind Source Separation Method for Convolutive Mixtures of Images.- Astrophysical Source Separation Using Particle Filters.- Independent Component Analysis in the Watermarking of Digital Images.- Spatio-chromatic ICA of a Mosaiced Color Image.- An Extended Maximum Likelihood Approach for the Robust Blind Separation of Autocorrelated Images from Noisy Mixtures.- Blind Separation of Spatio-temporal Data Sources.- Data Hiding in Independent Components of Video.- Biomedical Applications.- 3D Spatial Analysis of fMRI Data on a Word Perception Task.- Decomposition of Synthetic Multi-channel Surface-Electromyogram Using Independent Component Analysis.- Denoising Using Local ICA and a Generalized Eigendecomposition with Time-Delayed Signals.- MEG/EEG Source Localization Using Spatio-temporal Sparse Representations.- Reliable Measurement of Cortical Flow Patterns Using Complex Independent Component Analysis of Electroencephalographic Signals.- Sensor Array and Electrode Selection for Non-invasive Fetal Electrocardiogram Extraction by Independent Component Analysis.- A Comparison of Time Structure and Statistically Based BSS Methods in the Context of Long-Term Epileptiform EEG Recordings.- A Framework for Evaluating ICA Methods of Artifact Removal from Multichannel EEG.- A New Method for Eliminating Stimulus Artifact in Transient Evoked Otoacoustic Emission Using ICA.- An Efficient Time-Frequency Approach to Blind Source Separation Based on Wavelets.- Blind Deconvolution of Close-to-Orthogonal Pulse Sources Applied to Surface Electromyograms.- Denoising Mammographic Images Using ICA.- Independent Component Analysis of Pulse Oximetry Signals Based on Derivative Skew.- Mixing Matrix Pseudostationarity and ECG Preprocessing Impact on ICA-Based Atrial Fibrillation Analysis.- 'Signal Subspace' Blind Source Separation Applied to Fetal Magnetocardiographic Signals Extraction.- Suppression of Ventricular Activity in the Surface Electrocardiogram of Atrial Fibrillation.- Unraveling Spatio-temporal Dynamics in fMRI Recordings Using Complex ICA.- Wavelet Domain Blind Signal Separation to Analyze Supraventricular Arrhythmias from Holter Registers.- Other Applications.- A New Auditory-Based Index to Evaluate the Blind Separation Performance of Acoustic Mixtures.- An Application of ICA to Identify Vibratory Low-Level Signals Generated by Termites.- Application of Blind Source Separation to a Novel Passive Location.- Blind Source Separation in the Adaptive Reduction of Inter-channel Interference for OFDM.- BSS for Series of Electron Energy Loss Spectra.- HOS Based Distinctive Features for Preliminary Signal Classification.- ICA as a Preprocessing Technique for Classification.- Joint Delay Tracking and Interference Cancellation in DS-CDMA Systems Using Successive ICA for Oversaturated Data.- Layered Space Frequency Equalisation for MIMO-MC-CDMA Systems in Frequency Selective Fading Channels.- Multiuser Detection and Channel Estimation in MIMO OFDM Systems via Blind Source Separation.- Music Transcription with ISA and HMM.- On Shift-Invariant Sparse Coding.- Reliability in ICA-Based Text Classification.- Source Separation on Astrophysical Data Sets from the WMAP Satellite.- Multidimensional ICA for the Separation of Atrial and Ventricular Activities from Single Lead ECGs in Paroxysmal Atrial Fibrillation Episodes.- Music Indexing Using Independent Component Analysis with Pseudo-generated Sources.- Invited Contributions.- Lie Group Methods for Optimization with Orthogonality Constraints.- A Hierarchical ICA Method for Unsupervised Learning of Nonlinear Dependencies in Natural Images.


Titel: Independent Component Analysis and Blind Signal Separation
Untertitel: Fifth International Conference, ICA 2004, Granada, Spain, September 22-24, 2004, Proceedings
EAN: 9783540230564
ISBN: 978-3-540-23056-4
Format: Kartonierter Einband
Herausgeber: Springer, Berlin
Genre: Informatik
Anzahl Seiten: 1270
Gewicht: 1420g
Größe: H235mm x B235mm
Jahr: 2004
Untertitel: Englisch
Auflage: 2004

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