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Source Separation and Machine Learning

Source Separation and Machine Learning Book
Author : Jen-Tzung Chien
Publisher : Academic Press
Release : 2018-11-01
ISBN : 0128045779
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot
Publisher : John Wiley & Sons
Release : 2018-10-22
ISBN : 1119279895
Language : En, Es, Fr & De

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Book Description :

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.

Machine Learning Algorithms for Independent Vector Analysis and Blind Source Separation

Machine Learning Algorithms for Independent Vector Analysis and Blind Source Separation Book
Author : In Tae Lee
Publisher : Unknown
Release : 2009
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Blind signal separation (BSS) aims at recovering unknown source signals from the observed sensor signals where the mixing process is also unknown. As a popular method to solve this problem, independent component analysis (ICA) maximizes the mutual independence among, or equivalently the non-Gaussianity of, the signals and has been very successful especially when the unknown mixing process is instantaneous. In most realistic situations, however, there are time delay and reverberations which involve long filter lengths in the time domain. Such convolutive BSS problems are often tackled in the frequency domain, or short-time Fourier transform (STFT) domain, mainly because the convolutive mixture model can be approximated to bin-wise instantaneous mixtures given the frame size is long enough to cover the main part of the convolved impulse responses. While the bin-wise instantaneous mixtures can be separated by the ICA algorithms for complex-valued variables, there are several factors that have significant influence on the final separation performance, which are the permutation problem, incomplete bin-wise separation, and noise. Permutation problem refers to the random alignment of the STFT components that are separated by ICA. It is due to the permutation indeterminacy of ICA and it hinders proper reconstruction of the original time-domain signals. To solve this problem, a multidimensional ICA framework that is called independent vector analysis (IVA) has been proposed. IVA exploits the mutual dependence among the STFT components originating from the same source and employs a multivariate dependence model. In this thesis, various dependence models and methods are proposed in the framework of IVA to solve the convolutive BSS problem, which include Lp-norm invariant joint densities, density functions represented by overlapped cliques in graphical models, Newton's update optimization, and an EM algorithm using a mixture of multivariate Gaussians prior where Gaussian noise is added in the model. While IVA is an effective framework to solve the convolutive BSS, the high dimensionality in the STFT domain makes it difficult to model the joint probability density function (PDF) of the fullband STFT components. On the other hand, bin-wise separation is a simpler task for which a permutation correction algorithm has to follow. For permutation correction, overall measures of magnitude correlation have been popular. However, the positive correlation is stronger between STFT components that are close to each other and correlation is a measure computed pair-wise. Thus, in this thesis, subband likelihood functions are proposed for the permutation correction which is fast to obtain and robust in solving the permutation problem.

Unsupervised Signal Processing

Unsupervised Signal Processing Book
Author : João Marcos Travassos Romano,Romis Attux,Charles Casimiro Cavalcante,Ricardo Suyama
Publisher : CRC Press
Release : 2018-09-03
ISBN : 1420019465
Language : En, Es, Fr & De

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Book Description :

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.

Independent Component Analysis and Signal Separation

Independent Component Analysis and Signal Separation Book
Author : Tulay Adali,Christian Jutten,Joao Marcos Travassos Romano,Allan Kardec Barros
Publisher : Springer Science & Business Media
Release : 2009-02-25
ISBN : 3642005985
Language : En, Es, Fr & De

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Book Description :

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.

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

Information Retrieval from Marine Soundscape by Using Machine Learning based Source Separation Book
Author : Tzu-Hao Lin,Tomonari Akamatsu,Yu Tsao,Katsunori Fujikura
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Information Retrieval from Marine Soundscape by Using Machine Learning based Source Separation book written by Tzu-Hao Lin,Tomonari Akamatsu,Yu Tsao,Katsunori Fujikura, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Nonlinear Blind Source Separation and Blind Mixture Identification

Nonlinear Blind Source Separation and Blind Mixture Identification Book
Author : Yannick Deville,Leonardo Tomazeli Duarte,Shahram Hosseini
Publisher : Springer
Release : 2021-02-03
ISBN : 9783030649760
Language : En, Es, Fr & De

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Book Description :

This book provides a detailed survey of the methods that were recently developed to handle advanced versions of the blind source separation problem, which involve several types of nonlinear mixtures. Another attractive feature of the book is that it is based on a coherent framework. More precisely, the authors first present a general procedure for developing blind source separation methods. Then, all reported methods are defined with respect to this procedure. This allows the reader not only to more easily follow the description of each method but also to see how these methods relate to one another. The coherence of this book also results from the fact that the same notations are used throughout the chapters for the quantities (source signals and so on) that are used in various methods. Finally, among the quite varied types of processing methods that are presented in this book, a significant part of this description is dedicated to methods based on artificial neural networks, especially recurrent ones, which are currently of high interest to the data analysis and machine learning community in general, beyond the more specific signal processing and blind source separation communities.

Musical Source Separation with Deep Learning and Large scale Datasets

Musical Source Separation with Deep Learning and Large scale Datasets Book
Author : Andreas Jansson
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Musical Source Separation with Deep Learning and Large scale Datasets book written by Andreas Jansson, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Handbook of Blind Source Separation

Handbook of Blind Source Separation Book
Author : Pierre Comon,Christian Jutten
Publisher : Academic Press
Release : 2010-02-17
ISBN : 9780080884943
Language : En, Es, Fr & De

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Book Description :

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

Blind Identification and Separation of Complex valued Signals

Blind Identification and Separation of Complex valued Signals Book
Author : Eric Moreau,Tülay Adali
Publisher : John Wiley & Sons
Release : 2013-10-07
ISBN : 1848214596
Language : En, Es, Fr & De

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Book Description :

Blind identification consists of estimating a multi-dimensional system only through the use of its output, and source separation, the blind estimation of the inverse of the system. Estimation is generally carried out using different statistics of the output. The authors of this book consider the blind identification and source separation problem in the complex-domain, where the available statistical properties are richer and include non-circularity of the sources – underlying components. They define identifiability conditions and present state-of-the-art algorithms that are based on algebraic methods as well as iterative algorithms based on maximum likelihood theory. Contents 1. Mathematical Preliminaries. 2. Estimation by Joint Diagonalization. 3. Maximum Likelihood ICA. About the Authors Eric Moreau is Professor of Electrical Engineering at the University of Toulon, France. His research interests concern statistical signal processing, high order statistics and matrix/tensor decompositions with applications to data analysis, telecommunications and radar. Tülay Adali is Professor of Electrical Engineering and Director of the Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County, USA. Her research interests concern statistical and adaptive signal processing, with an emphasis on nonlinear and complex-valued signal processing, and applications in biomedical data analysis and communications. Blind identification consists of estimating a multidimensional system through the use of only its output. Source separation is concerned with the blind estimation of the inverse of the system. The estimation is generally performed by using different statistics of the outputs. The authors consider the blind estimation of a multiple input/multiple output (MIMO) system that mixes a number of underlying signals of interest called sources. They also consider the case of direct estimation of the inverse system for the purpose of source separation. They then describe the estimation theory associated with the identifiability conditions and dedicated algebraic algorithms. The algorithms depend critically on (statistical and/or time frequency) properties of complex sources that will be precisely described.

Machine Learning ECML 2007

Machine Learning  ECML 2007 Book
Author : Joost N. Kok,Jacek Koronacki,Ramon Lopez de Mantaras,Dunja Mladenic,Stan Matwin
Publisher : Springer Science & Business Media
Release : 2007-09-05
ISBN : 3540749578
Language : En, Es, Fr & De

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Book Description :

The two premier annual European conferences in the areas of machine learning and data mining have been collocated ever since the ?rst joint conference in Freiburg, 2001. The European Conference on Machine Learning (ECML) traces its origins to 1986, when the ?rst European Working Session on Learning was held in Orsay, France. The European Conference on Principles and Practice of KnowledgeDiscoveryinDatabases(PKDD) was?rstheldin1997inTrondheim, Norway. Over the years, the ECML/PKDD series has evolved into one of the largest and most selective international conferences in machine learning and data mining. In 2007, the seventh collocated ECML/PKDD took place during September 17–21 on the centralcampus of WarsawUniversityand in the nearby Staszic Palace of the Polish Academy of Sciences. The conference for the third time used a hierarchical reviewing process. We nominated 30 Area Chairs, each of them responsible for one sub-?eld or several closely related research topics. Suitable areas were selected on the basis of the submission statistics for ECML/PKDD 2006 and for last year’s International Conference on Machine Learning (ICML 2006) to ensure a proper load balance amongtheAreaChairs.AjointProgramCommittee(PC)wasnominatedforthe two conferences, consisting of some 300 renowned researchers, mostly proposed by the Area Chairs. This joint PC, the largest of the series to date, allowed us to exploit synergies and deal competently with topic overlaps between ECML and PKDD. ECML/PKDD 2007 received 592 abstract submissions. As in previous years, toassistthereviewersandtheAreaChairsintheir?nalrecommendationauthors had the opportunity to communicate their feedback after the reviewing phase.

Audio Source Separation and Speech Enhancement

Audio Source Separation and Speech Enhancement Book
Author : Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot
Publisher : John Wiley & Sons
Release : 2018-07-24
ISBN : 1119279887
Language : En, Es, Fr & De

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Book Description :

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.

Intelligence Science and Big Data Engineering Big Data and Machine Learning

Intelligence Science and Big Data Engineering  Big Data and Machine Learning Book
Author : Zhen Cui,Jinshan Pan,Shanshan Zhang,Liang Xiao,Jian Yang
Publisher : Springer Nature
Release : 2019-11-28
ISBN : 3030362043
Language : En, Es, Fr & De

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Book Description :

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.

Audio Source Separation

Audio Source Separation Book
Author : Shoji Makino
Publisher : Springer
Release : 2018-03-01
ISBN : 3319730312
Language : En, Es, Fr & De

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Book Description :

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.

Machine Learning Algorithms for Problem Solving in Computational Applications Intelligent Techniques

Machine Learning Algorithms for Problem Solving in Computational Applications  Intelligent Techniques Book
Author : Kulkarni, Siddhivinayak
Publisher : IGI Global
Release : 2012-06-30
ISBN : 1466618345
Language : En, Es, Fr & De

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Book Description :

Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.

Improving Acoustic Monitoring of Biodiversity Using Deep Learning based Source Separation Algorithms

Improving Acoustic Monitoring of Biodiversity Using Deep Learning based Source Separation Algorithms Book
Author : Mao-Ning Tuanmu,Tzu-Hao Lin,Joe Huang,Yu Tsao,Chia-Yun Lee
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Improving Acoustic Monitoring of Biodiversity Using Deep Learning based Source Separation Algorithms book written by Mao-Ning Tuanmu,Tzu-Hao Lin,Joe Huang,Yu Tsao,Chia-Yun Lee, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

EEG Signal Processing and Machine Learning

EEG Signal Processing and Machine Learning Book
Author : Saeid Sanei,Jonathon A. Chambers
Publisher : John Wiley & Sons
Release : 2021-09-23
ISBN : 1119386934
Language : En, Es, Fr & De

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Book Description :

EEG Signal Processing and Machine Learning Explore cutting edge techniques at the forefront of electroencephalogram research and artificial intelligence from leading voices in the field The newly revised Second Edition of EEG Signal Processing and Machine Learning delivers an inclusive and thorough exploration of new techniques and outcomes in electroencephalogram (EEG) research in the areas of analysis, processing, and decision making about a variety of brain states, abnormalities, and disorders using advanced signal processing and machine learning techniques. The book content is substantially increased upon that of the first edition and, while it retains what made the first edition so popular, is composed of more than 50% new material. The distinguished authors have included new material on tensors for EEG analysis and sensor fusion, as well as new chapters on mental fatigue, sleep, seizure, neurodevelopmental diseases, BCI, and psychiatric abnormalities. In addition to including a comprehensive chapter on machine learning, machine learning applications have been added to almost all the chapters. Moreover, multimodal brain screening, such as EEG-fMRI, and brain connectivity have been included as two new chapters in this new edition. Readers will also benefit from the inclusion of: A thorough introduction to EEGs, including neural activities, action potentials, EEG generation, brain rhythms, and EEG recording and measurement An exploration of brain waves, including their generation, recording, and instrumentation, abnormal EEG patterns and the effects of ageing and mental disorders A treatment of mathematical models for normal and abnormal EEGs Discussions of the fundamentals of EEG signal processing, including statistical properties, linear and nonlinear systems, frequency domain approaches, tensor factorization, diffusion adaptive filtering, deep neural networks, and complex-valued signal processing Perfect for biomedical engineers, neuroscientists, neurophysiologists, psychiatrists, engineers, students and researchers in the above areas, the Second Edition of EEG Signal Processing and Machine Learning will also earn a place in the libraries of undergraduate and postgraduate students studying Biomedical Engineering, Neuroscience and Epileptology.

Multimodal Behavior Analysis in the Wild

Multimodal Behavior Analysis in the Wild Book
Author : Xavier Alameda-Pineda,Elisa Ricci,Nicu Sebe
Publisher : Academic Press
Release : 2018-11-13
ISBN : 0128146028
Language : En, Es, Fr & De

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Book Description :

Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification), and high level (personality and emotion recognition), providing insights on how to exploit inter-level and intra-level links. This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild. It is suitable for researchers and graduate students in the fields of computer vision, audio processing, pattern recognition, machine learning and social signal processing. Gives a comprehensive collection of information on the state-of-the-art, limitations, and challenges associated with extracting behavioral cues from real-world scenarios Presents numerous applications on how different behavioral cues have been successfully extracted from different data sources Provides a wide variety of methodologies used to extract behavioral cues from multi-modal data

Independent Component Analysis and Blind Signal Separation

Independent Component Analysis and Blind Signal Separation Book
Author : Justinian Rosca,Deniz Erdogmus,Jose C. Principe,Simon Haykin
Publisher : Springer Science & Business Media
Release : 2006-02-13
ISBN : 3540326308
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2006, held in Charleston, SC, USA, in March 2006. The 120 revised papers presented were carefully reviewed and selected from 183 submissions. The papers are organized in topical sections on algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing.

Blind Source Separation

Blind Source Separation Book
Author : Yong Xiang,Dezhong Peng,Zuyuan Yang
Publisher : Springer
Release : 2014-09-16
ISBN : 9812872272
Language : En, Es, Fr & De

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Book Description :

This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind separation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.