Source Separation and Machine Learning

Source Separation and Machine Learning
Author: Jen-Tzung Chien
Publsiher: Academic Press
Total Pages: 384
Release: 2018-11-01
ISBN 10: 0128045779
ISBN 13: 9780128045770
Language: EN, FR, DE, ES & NL

Source Separation and Machine Learning Book Review:

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation. Emphasizes the modern model-based Blind Source Separation (BSS) which closely connects the latest research topics of BSS and Machine Learning Includes coverage of Bayesian learning, sparse learning, online learning, discriminative learning and deep learning Presents a number of case studies of model-based BSS (categorizing them into four modern models - ICA, NMF, NTF and DNN), using a variety of learning algorithms that provide solutions for the construction of BSS systems

Audio Source Separation and Speech Enhancement

Audio Source Separation and Speech Enhancement
Author: Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot
Publsiher: John Wiley & Sons
Total Pages: 504
Release: 2018-10-22
ISBN 10: 1119279895
ISBN 13: 9781119279891
Language: EN, FR, DE, ES & NL

Audio Source Separation and Speech Enhancement Book Review:

Learn the technology behind hearing aids, Siri, and Echo Audio source separation and speech enhancement aim to extract one or more source signals of interest from an audio recording involving several sound sources. These technologies are among the most studied in audio signal processing today and bear a critical role in the success of hearing aids, hands-free phones, voice command and other noise-robust audio analysis systems, and music post-production software. Research on this topic has followed three convergent paths, starting with sensor array processing, computational auditory scene analysis, and machine learning based approaches such as independent component analysis, respectively. This book is the first one to provide a comprehensive overview by presenting the common foundations and the differences between these techniques in a unified setting. Key features: Consolidated perspective on audio source separation and speech enhancement. Both historical perspective and latest advances in the field, e.g. deep neural networks. Diverse disciplines: array processing, machine learning, and statistical signal processing. Covers the most important techniques for both single-channel and multichannel processing. This book provides both introductory and advanced material suitable for people with basic knowledge of signal processing and machine learning. Thanks to its comprehensiveness, it will help students select a promising research track, researchers leverage the acquired cross-domain knowledge to design improved techniques, and engineers and developers choose the right technology for their target application scenario. It will also be useful for practitioners from other fields (e.g., acoustics, multimedia, phonetics, and musicology) willing to exploit audio source separation or speech enhancement as pre-processing tools for their own needs.

Unsupervised Signal Processing

Unsupervised Signal Processing
Author: João Marcos Travassos Romano,Romis Attux,Charles Casimiro Cavalcante,Ricardo Suyama
Publsiher: CRC Press
Total Pages: 340
Release: 2018-09-03
ISBN 10: 1420019465
ISBN 13: 9781420019469
Language: EN, FR, DE, ES & NL

Unsupervised Signal Processing Book Review:

Unsupervised Signal Processing: Channel Equalization and Source Separation provides a unified, systematic, and synthetic presentation of the theory of unsupervised signal processing. Always maintaining the focus on a signal processing-oriented approach, this book describes how the subject has evolved and assumed a wider scope that covers several topics, from well-established blind equalization and source separation methods to novel approaches based on machine learning and bio-inspired algorithms. From the foundations of statistical and adaptive signal processing, the authors explore and elaborate on emerging tools, such as machine learning-based solutions and bio-inspired methods. With a fresh take on this exciting area of study, this book: Provides a solid background on the statistical characterization of signals and systems and on linear filtering theory Emphasizes the link between supervised and unsupervised processing from the perspective of linear prediction and constrained filtering theory Addresses key issues concerning equilibrium solutions and equivalence relationships in the context of unsupervised equalization criteria Provides a systematic presentation of source separation and independent component analysis Discusses some instigating connections between the filtering problem and computational intelligence approaches. Building on more than a decade of the authors’ work at DSPCom laboratory, this book applies a fresh conceptual treatment and mathematical formalism to important existing topics. The result is perhaps the first unified presentation of unsupervised signal processing techniques—one that addresses areas including digital filters, adaptive methods, and statistical signal processing. With its remarkable synthesis of the field, this book provides a new vision to stimulate progress and contribute to the advent of more useful, efficient, and friendly intelligent systems.

Python Machine Learning Cookbook

Python Machine Learning Cookbook
Author: Prateek Joshi
Publsiher: Packt Publishing Ltd
Total Pages: 304
Release: 2016-06-23
ISBN 10: 1786467682
ISBN 13: 9781786467683
Language: EN, FR, DE, ES & NL

Python Machine Learning Cookbook Book Review:

100 recipes that teach you how to perform various machine learning tasks in the real world About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide Learn about perceptrons and see how they are used to build neural networks Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques Who This Book Is For This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code. What You Will Learn Explore classification algorithms and apply them to the income bracket estimation problem Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Explore data visualization techniques to interact with your data in diverse ways Find out how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Analyze stock market data using Conditional Random Fields Work with image data and build systems for image recognition and biometric face recognition Grasp how to use deep neural networks to build an optical character recognition system In Detail Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. Style and approach You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book.

Independent Component Analysis and Signal Separation

Independent Component Analysis and Signal Separation
Author: Mike E. Davies,Christopher C. James,Samer A. Abdallah,Mark D. Plumbley
Publsiher: Springer Science & Business Media
Total Pages: 847
Release: 2007-08-28
ISBN 10: 3540744932
ISBN 13: 9783540744931
Language: EN, FR, DE, ES & NL

Independent Component Analysis and Signal Separation Book Review:

This book constitutes the refereed proceedings of the 7th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2007, held in London, UK, in September 2007. It covers algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing.

Handbook of Blind Source Separation

Handbook of Blind Source Separation
Author: Pierre Comon,Christian Jutten
Publsiher: Academic Press
Total Pages: 856
Release: 2010-02-17
ISBN 10: 9780080884943
ISBN 13: 0080884946
Language: EN, FR, DE, ES & NL

Handbook of Blind Source Separation Book Review:

Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications

Audio Source Separation

Audio Source Separation
Author: Shoji Makino
Publsiher: Springer
Total Pages: 385
Release: 2018-03-01
ISBN 10: 3319730312
ISBN 13: 9783319730318
Language: EN, FR, DE, ES & NL

Audio Source Separation Book Review:

This book provides the first comprehensive overview of the fascinating topic of audio source separation based on non-negative matrix factorization, deep neural networks, and sparse component analysis. The first section of the book covers single channel source separation based on non-negative matrix factorization (NMF). After an introduction to the technique, two further chapters describe separation of known sources using non-negative spectrogram factorization, and temporal NMF models. In section two, NMF methods are extended to multi-channel source separation. Section three introduces deep neural network (DNN) techniques, with chapters on multichannel and single channel separation, and a further chapter on DNN based mask estimation for monaural speech separation. In section four, sparse component analysis (SCA) is discussed, with chapters on source separation using audio directional statistics modelling, multi-microphone MMSE-based techniques and diffusion map methods. The book brings together leading researchers to provide tutorial-like and in-depth treatments on major audio source separation topics, with the objective of becoming the definitive source for a comprehensive, authoritative, and accessible treatment. This book is written for graduate students and researchers who are interested in audio source separation techniques based on NMF, DNN and SCA.

Independent Component Analysis and Signal Separation

Independent Component Analysis and Signal Separation
Author: Tulay Adali,Christian Jutten,Joao Marcos Travassos Romano,Allan Kardec Barros
Publsiher: Springer Science & Business Media
Total Pages: 785
Release: 2009-02-25
ISBN 10: 3642005985
ISBN 13: 9783642005985
Language: EN, FR, DE, ES & NL

Independent Component Analysis and Signal Separation Book Review:

This volume contains the papers presented at the 8th International Conf- ence on Independent Component Analysis (ICA) and Source Separation held in Paraty, Brazil, March 15–18, 2009. This year's event resulted from scienti?c collaborations between a team of researchers from ?ve di?erent Brazilian u- versities and received the support of the Brazilian Telecommunications Society (SBrT) as well as the ?nancial sponsorship of CNPq, CAPES and FAPERJ. Independent component analysis and signal separation is one of the most - citing current areas of research in statistical signal processing and unsupervised machine learning. The area has received attention from severalresearchcom- nities including machine learning, neural networks, statistical signal processing and Bayesian modeling. Independent component analysis and signal separation has applications at the intersection of many science 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 signals, and time series.

Latent Variable Analysis and Signal Separation

Latent Variable Analysis and Signal Separation
Author: Fabian Theis,Andrzej Cichocki,Arie Yeredor,Michael Zibulevsky
Publsiher: Springer Science & Business Media
Total Pages: 538
Release: 2012-03-01
ISBN 10: 3642285503
ISBN 13: 9783642285509
Language: EN, FR, DE, ES & NL

Latent Variable Analysis and Signal Separation Book Review:

This book constitutes the proceedings of the 10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012, held in Tel Aviv, Israel, in March 2012. The 20 revised full papers presented together with 42 revised poster papers, 1 keynote lecture, and 2 overview papers for the regular, as well as for the special session were carefully reviewed and selected from numerous submissions. Topics addressed are ranging from theoretical issues such as causality analysis and measures, through novel methods for employing the well-established concepts of sparsity and non-negativity for matrix and tensor factorization, down to a variety of related applications ranging from audio and biomedical signals to precipitation analysis.

Artificial Intelligence and Soft Computing — ICAISC 2004

Artificial Intelligence and Soft Computing — ICAISC 2004
Author: Leszek Rutkowski,Jörg Siekmann,Ryszard Tadeusiewicz,Lotfi A. Zadeh
Publsiher: Springer
Total Pages: 1210
Release: 2004-05-18
ISBN 10: 3540248447
ISBN 13: 9783540248446
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Soft Computing — ICAISC 2004 Book Review:

This book constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004, held in Zakopane, Poland in June 2004. The 172 revised contributed papers presented together with 17 invited papers were carefully reviewed and selected from 250 submissions. The papers are organized in topical sections on neural networks, fuzzy systems, evolutionary algorithms, rough sets, soft computing in classification, image processing, robotics, multiagent systems, problems in AI, intelligent control, modeling and system identification, medical applications, mechanical applications, and applications in various fields.

Blind Speech Separation

Blind Speech Separation
Author: Shoji Makino,Te-Won Lee,Hiroshi Sawada
Publsiher: Springer Science & Business Media
Total Pages: 432
Release: 2007-09-07
ISBN 10: 1402064799
ISBN 13: 9781402064791
Language: EN, FR, DE, ES & NL

Blind Speech Separation Book Review:

This is the world’s first edited book on independent component analysis (ICA)-based blind source separation (BSS) of convolutive mixtures of speech. This book brings together a small number of leading researchers to provide tutorial-like and in-depth treatment on major ICA-based BSS topics, with the objective of becoming the definitive source for current, comprehensive, authoritative, and yet accessible treatment.

Machine Learning: ECML 2007

Machine Learning: ECML 2007
Author: Joost N. Kok,Jacek Koronacki,Ramon Lopez de Mantaras,Stan Matwin,Dunja Mladenic
Publsiher: Springer
Total Pages: 812
Release: 2007-09-08
ISBN 10: 3540749586
ISBN 13: 9783540749585
Language: EN, FR, DE, ES & NL

Machine Learning: ECML 2007 Book Review:

This book constitutes the refereed proceedings of the 18th European Conference on Machine Learning, ECML 2007, held in Warsaw, Poland, September 2007, jointly with PKDD 2007. The 41 revised full papers and 37 revised short papers presented together with abstracts of four invited talks were carefully reviewed and selected from 592 abstracts submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.

Latent Variable Analysis and Signal Separation

Latent Variable Analysis and Signal Separation
Author: Vincent Vigneron,Vicente Zarzoso,Eric Moreau,Rémi Gribonval,Emmanuel Vincent
Publsiher: Springer Science & Business Media
Total Pages: 655
Release: 2010-09-27
ISBN 10: 364215994X
ISBN 13: 9783642159947
Language: EN, FR, DE, ES & NL

Latent Variable Analysis and Signal Separation Book Review:

Thisvolumecollectsthepaperspresentedatthe9thInternationalConferenceon Latent Variable Analysis and Signal Separation,LVA/ICA 2010. The conference was organized by INRIA, the French National Institute for Computer Science and Control,and was held in Saint-Malo, France, September 27–30,2010,at the Palais du Grand Large. Tenyearsafterthe?rstworkshoponIndependent Component Analysis(ICA) in Aussois, France, the series of ICA conferences has shown the liveliness of the community of theoreticians and practitioners working in this ?eld. While ICA and blind signal separation have become mainstream topics, new approaches have emerged to solve problems involving signal mixtures or various other types of latent variables: semi-blind models, matrix factorization using sparse com- nent analysis, non-negative matrix factorization, probabilistic latent semantic indexing, tensor decompositions, independent vector analysis, independent s- space analysis, and so on. To re?ect this evolution towards more general latent variable analysis problems in signal processing, the ICA International Steering Committee decided to rename the 9th instance of the conference LVA/ICA. From more than a hundred submitted papers, 25 were accepted as oral p- sentationsand53 asposter presentations. Thecontent ofthis volumefollowsthe conference schedule, resulting in 14 chapters. The papers collected in this v- ume demonstrate that the research activity in the ?eld continues to range from abstract concepts to the most concrete and applicable questions and consid- ations. Speech and audio, as well as biomedical applications, continue to carry the mass of the applications considered.

Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Intelligence Science and Big Data Engineering. Big Data and Machine Learning
Author: Zhen Cui,Jinshan Pan,Shanshan Zhang,Liang Xiao,Jian Yang
Publsiher: Springer Nature
Total Pages: 455
Release: 2019-11-28
ISBN 10: 3030362043
ISBN 13: 9783030362041
Language: EN, FR, DE, ES & NL

Intelligence Science and Big Data Engineering. Big Data and Machine Learning Book Review:

The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.

2017 4th International Conference on Systems and Informatics (ICSAI)

2017 4th International Conference on Systems and Informatics (ICSAI)
Author: IEEE Staff
Publsiher: Anonim
Total Pages: 329
Release: 2017-11-11
ISBN 10: 9781538611081
ISBN 13: 1538611082
Language: EN, FR, DE, ES & NL

2017 4th International Conference on Systems and Informatics (ICSAI) Book Review:

ICSAI aims to be a premier international forum for scientists and researchers to present the state of the art of systems and informatics Topics of interest include Control and Automation Systems, Power and Energy Systems, Intelligent Systems, Computer Systems and Applications, Communications and Networking, Image, Video, and Signal Processing, Data Engineering and Data Mining, Software Engineering, and Industrial Informatics

EEG Signal Processing and Machine Learning

EEG Signal Processing and Machine Learning
Author: Saeid Sanei,Jonathon A. Chambers
Publsiher: Wiley
Total Pages: 380
Release: 2021-06-08
ISBN 10: 9781119386940
ISBN 13: 1119386942
Language: EN, FR, DE, ES & NL

EEG Signal Processing and Machine Learning Book Review:

The book aims at describing new techniques and outcomes in electroencephalogram (EEG) research mainly in analysis, processing and decision making about various brain states, abnormalities, and disorders by means of advance signal processing and machine learning techniques respectively.

Handbook of Blind Source Separation

Handbook of Blind Source Separation
Author: Pierre Comon,Christian Jutten
Publsiher: Academic Press
Total Pages: 856
Release: 2010-02-17
ISBN 10: 9780080884943
ISBN 13: 0080884946
Language: EN, FR, DE, ES & NL

Handbook of Blind Source Separation Book Review:

Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications

Information Retrieval from Marine Soundscape by Using Machine Learning-based Source Separation

Information Retrieval from Marine Soundscape by Using Machine Learning-based Source Separation
Author: Tzu-Hao Lin,Tomonari Akamatsu,Yu Tsao,Katsunori Fujikura
Publsiher: Anonim
Total Pages: 329
Release: 2018
ISBN 10:
ISBN 13: OCLC:1189396448
Language: EN, FR, DE, ES & NL

Information Retrieval from Marine Soundscape by Using Machine Learning-based Source Separation Book Review:

Blind Source Separation

Blind Source Separation
Author: Xianchuan Yu,Dan Hu,Jindong Xu
Publsiher: John Wiley & Sons
Total Pages: 416
Release: 2013-12-13
ISBN 10: 1118679873
ISBN 13: 9781118679876
Language: EN, FR, DE, ES & NL

Blind Source Separation Book Review:

A systematic exploration of both classic and contemporaryalgorithms in blind source separation with practical casestudies The book presents an overview of Blind Source Separation, arelatively new signal processing method. Due to themultidisciplinary nature of the subject, the book has been writtenso as to appeal to an audience from very different backgrounds.Basic mathematical skills (e.g. on matrix algebra and foundationsof probability theory) are essential in order to understand thealgorithms, although the book is written in an introductory,accessible style. This book offers a general overview of the basics of BlindSource Separation, important solutions and algorithms, and in-depthcoverage of applications in image feature extraction, remotesensing image fusion, mixed-pixel decomposition of SAR images,image object recognition fMRI medical image processing, geochemicaland geophysical data mining, mineral resources prediction andgeoanomalies information recognition. Firstly, the background andtheory basics of blind source separation are introduced, whichprovides the foundation for the following work. Matrix operation,foundations of probability theory and information theory basics areincluded here. There follows the fundamental mathematical model andfairly new but relatively established blind source separationalgorithms, such as Independent Component Analysis (ICA) and itsimproved algorithms (Fast ICA, Maximum Likelihood ICA, OvercompleteICA, Kernel ICA, Flexible ICA, Non-negative ICA, Constrained ICA,Optimised ICA). The last part of the book considers the very recentalgorithms in BSS e.g. Sparse Component Analysis (SCA) andNon-negative Matrix Factorization (NMF). Meanwhile, in-depth casesare presented for each algorithm in order to help the readerunderstand the algorithm and its application field. A systematic exploration of both classic and contemporaryalgorithms in blind source separation with practical casestudies Presents new improved algorithms aimed at differentapplications, such as image feature extraction, remote sensingimage fusion, mixed-pixel decomposition of SAR images, image objectrecognition, and MRI medical image processing With applications in geochemical and geophysical data mining,mineral resources prediction and geoanomalies informationrecognition Written by an expert team with accredited innovations in blindsource separation and its applications in natural science Accompanying website includes a software system providing codesfor most of the algorithms mentioned in the book, enhancing thelearning experience Essential reading for postgraduate students and researchersengaged in the area of signal processing, data mining, imageprocessing and recognition, information, geosciences, lifesciences.

Independent Component Analysis and Blind Signal Separation

Independent Component Analysis and Blind Signal Separation
Author: Carlos G. Puntonet
Publsiher: Springer Science & Business Media
Total Pages: 1266
Release: 2004-09-17
ISBN 10: 3540230564
ISBN 13: 9783540230564
Language: EN, FR, DE, ES & NL

Independent Component Analysis and Blind Signal Separation Book Review:

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.