Independent Component Analysis and Blind Signal Separation [electronic resource] : 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006. Proceedings / edited by Justinian Rosca, Deniz Erdogmus, José C. Príncipe, Simon Haykin.

By: Rosca, Justinian [editor.]Contributor(s): Erdogmus, Deniz [editor.] | Príncipe, José C [editor.] | Haykin, Simon [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 3889Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006Description: XXI, 980 p. Also available online. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540326311Subject(s): Computer science | Software engineering | Coding theory | Computer software | Mathematical statistics | Computer Science | Special Purpose and Application-Based Systems | Algorithm Analysis and Problem Complexity | Computation by Abstract Devices | Coding and Information Theory | Statistics and Computing/Statistics Programs | Signal, Image and Speech ProcessingAdditional physical formats: Printed edition:: No titleDDC classification: 004.6 LOC classification: TK7874.6Online resources: Click here to access online
Contents:
Algorithms and Architectures -- Applications -- Medical Applications -- Speech and Signal Processing -- Theory -- Visual and Sensory Processing.
In: Springer eBooksSummary: This volume contains the papers presented at the 6th International Conference on Independent Component Analysis (ICA) and Blind Source Separation (BSS) organized in historic Charleston, SC, USA, March 5-8, 2006. The sixth edition of the conference has brought the latest developments in one of the most exciting areas of statistical signal processing/unsupervised machine learning. ICA theory has received attention from several research communities including machine learning, neural networks, statistical signal processing and Bayesian modeling. ICA/BSS has applications at the intersection of many s- ence and engineering disciplines concerned with understanding and extracting useful information from data as diverse as neuronal activity and brain images, bioinformatics, communications, the world wide-web, audio, video, sensor s- nals, or time series. Papers were solicited in all areas of independent component analysis and blind source separation, including the following: algorithms and architectures (e. g. s- tistical learning algorithms based on ICA and BSS using linear/nonlinear m- ture models, convolutive and noisy models, extensions of basic models, combinatorial optimization, kernel methods, graphical models), applications (innovative applications or ?elded systems that use ICA and BSS, including systems for acoustic signal separation, time series prediction, data mining, multimedia processing, telecommunications), medical applications (e. g. , bioinf- matics, neuroimaging, processing of electrocardiograms, electroencephalograms, magnetoencephalograms, and functional magnetic resonance imaging), speech and signal processing (e. g. , computational auditory speech analysis, source s- aration, auditory perception, coding, recognition, synthesis, denoising, segmen- tion, dynamic and temporal models), theory (e. g.
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Algorithms and Architectures -- Applications -- Medical Applications -- Speech and Signal Processing -- Theory -- Visual and Sensory Processing.

This volume contains the papers presented at the 6th International Conference on Independent Component Analysis (ICA) and Blind Source Separation (BSS) organized in historic Charleston, SC, USA, March 5-8, 2006. The sixth edition of the conference has brought the latest developments in one of the most exciting areas of statistical signal processing/unsupervised machine learning. ICA theory has received attention from several research communities including machine learning, neural networks, statistical signal processing and Bayesian modeling. ICA/BSS has applications at the intersection of many s- ence and engineering disciplines concerned with understanding and extracting useful information from data as diverse as neuronal activity and brain images, bioinformatics, communications, the world wide-web, audio, video, sensor s- nals, or time series. Papers were solicited in all areas of independent component analysis and blind source separation, including the following: algorithms and architectures (e. g. s- tistical learning algorithms based on ICA and BSS using linear/nonlinear m- ture models, convolutive and noisy models, extensions of basic models, combinatorial optimization, kernel methods, graphical models), applications (innovative applications or ?elded systems that use ICA and BSS, including systems for acoustic signal separation, time series prediction, data mining, multimedia processing, telecommunications), medical applications (e. g. , bioinf- matics, neuroimaging, processing of electrocardiograms, electroencephalograms, magnetoencephalograms, and functional magnetic resonance imaging), speech and signal processing (e. g. , computational auditory speech analysis, source s- aration, auditory perception, coding, recognition, synthesis, denoising, segmen- tion, dynamic and temporal models), theory (e. g.

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