Independent Component Analysis For Audio And Biosignal Applications
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Independent Component Analysis For Audio And Biosignal Applications
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Author : Ganesh R. Naik
language : en
Publisher: BoD – Books on Demand
Release Date : 2012-10-10
Independent Component Analysis For Audio And Biosignal Applications written by Ganesh R. Naik and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-10 with Medical categories.
Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image processing, telecommunications, medical signal processing and several data mining issues. This book brings the state-of-the-art of some of the most important current research of ICA related to Audio and Biomedical signal processing applications. The book is partly a textbook and partly a monograph. It is a textbook because it gives a detailed introduction to ICA applications. It is simultaneously a monograph because it presents several new results, concepts and further developments, which are brought together and published in the book.
Independent Component Analysis For Audio And Biosignal Applications
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Author : Ganesh R. Naik
language : en
Publisher: IntechOpen
Release Date : 2012-10-10
Independent Component Analysis For Audio And Biosignal Applications written by Ganesh R. Naik and has been published by IntechOpen this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-10 with Medical categories.
Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image processing, telecommunications, medical signal processing and several data mining issues. This book brings the state-of-the-art of some of the most important current research of ICA related to Audio and Biomedical signal processing applications. The book is partly a textbook and partly a monograph. It is a textbook because it gives a detailed introduction to ICA applications. It is simultaneously a monograph because it presents several new results, concepts and further developments, which are brought together and published in the book.
Audio Source Separation Using Independent Component Analysis And Beam Formation
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Author : Kishan Panaganti
language : en
Publisher: GRIN Verlag
Release Date : 2014-02-05
Audio Source Separation Using Independent Component Analysis And Beam Formation written by Kishan Panaganti and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-05 with Science categories.
Project Report from the year 2013 in the subject Audio Engineering, grade: 10, , course: ECE, language: English, abstract: Audio source separation is the problem of automated separation of audio sources present in a room, using a set of differently placed microphones, capturing the auditory scene. The whole problem resembles the task a human can solve in a cocktail party situation, where using two sensors (ears), the brain can focus on a specific source of interest, suppressing all other sources present (cocktail party problem). For computational and conceptual simplicity this problem is often represented as a linear transformation of the original audio signals. In other words, each component (multivariate signal) of the representation is a linear combination of the original variables (original subcomponents). In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents by assuming that the subcomponents are non-Gaussian signals and that they are all statistically independent from each other. Such a representation seems to capture the essential structure of the data in many applications. Here we separate audio using different criteria suggested for ICA, being PCA (Principal Component Analysis), Non-gaussianity maximization using kurtosis and neg-entropy methods, frequency domain approach using non-gaussianity maximization and beamforming.
Latent Variable Analysis And Signal Separation
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Author : Petr Tichavský
language : en
Publisher: Springer
Release Date : 2017-02-13
Latent Variable Analysis And Signal Separation written by Petr Tichavský and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-13 with Computers categories.
This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, held in Grenoble, France, in Feburary 2017. The 53 papers presented in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections named: tensor approaches; from source positions to room properties: learning methods for audio scene geometry estimation; tensors and audio; audio signal processing; theoretical developments; physics and bio signal processing; latent variable analysis in observation sciences; ICA theory and applications; and sparsity-aware signal processing.
Statistical Techniques For Neuroscientists
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Author : Young K. Truong
language : en
Publisher: CRC Press
Release Date : 2016-10-04
Statistical Techniques For Neuroscientists written by Young K. Truong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-04 with Mathematics categories.
Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron or large cluster (brain region) neuron activity. The statistical estimation and tests of hypotheses are based on the likelihood principle derived from stationary point processes and time series. Algorithms and software development are given in each chapter to reproduce the computer simulated results described therein. The book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and multimodal neuroimaging techniques. The author provides an overview of various methods being applied to specific research areas of neuroscience, emphasizing statistical principles and their software. The book includes examples and experimental data so that readers can understand the principles and master the methods. The first part of the book deals with the traditional multivariate time series analysis applied to the context of multichannel spike trains and fMRI using respectively the probability structures or likelihood associated with time-to-fire and discrete Fourier transforms (DFT) of point processes. The second part introduces a relatively new form of statistical spatiotemporal modeling for fMRI and EEG data analysis. In addition to neural scientists and statisticians, anyone wishing to employ intense computing methods to extract important features and information directly from data rather than relying heavily on models built on leading cases such as linear regression or Gaussian processes will find this book extremely helpful.
Multivariate Analysis For Neuroimaging Data
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Author : Atsushi Kawaguchi
language : en
Publisher: CRC Press
Release Date : 2021-07-01
Multivariate Analysis For Neuroimaging Data written by Atsushi Kawaguchi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-01 with Mathematics categories.
This book describes methods for statistical brain imaging data analysis from both the perspective of methodology and from the standpoint of application for software implementation in neuroscience research. These include those both commonly used (traditional established) and state of the art methods. The former is easier to do due to the availability of appropriate software. To understand the methods it is necessary to have some mathematical knowledge which is explained in the book with the help of figures and descriptions of the theory behind the software. In addition, the book includes numerical examples to guide readers on the working of existing popular software. The use of mathematics is reduced and simplified for non-experts using established methods, which also helps in avoiding mistakes in application and interpretation. Finally, the book enables the reader to understand and conceptualize the overall flow of brain imaging data analysis, particularly for statisticians and data-scientists unfamiliar with this area. The state of the art method described in the book has a multivariate approach developed by the authors’ team. Since brain imaging data, generally, has a highly correlated and complex structure with large amounts of data, categorized into big data, the multivariate approach can be used as dimension reduction by following the application of statistical methods. The R package for most of the methods described is provided in the book. Understanding the background theory is helpful in implementing the software for original and creative applications and for an unbiased interpretation of the output. The book also explains new methods in a conceptual manner. These methodologies and packages are commonly applied in life science data analysis. Advanced methods to obtain novel insights are introduced, thereby encouraging the development of new methods and applications for research into medicine as a neuroscience.
Blind Source Separation
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Author : Ganesh R. Naik
language : en
Publisher: Springer
Release Date : 2014-05-21
Blind Source Separation written by Ganesh R. Naik and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-21 with Technology & Engineering categories.
Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms and applications of BSS. Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.
Independent Component Analysis Ica
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Author : Addisson Salazar
language : en
Publisher:
Release Date : 2017
Independent Component Analysis Ica written by Addisson Salazar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Independent component analysis categories.
"This book embraces a significant vision of ICA that presents innovative theoretical and practical approaches. This book aims to be an updated and advanced source of knowledge to solve real-world problems efficiently based on ICA.The suitability of ICA for a given problem of data analysis can be posed from different perspectives considering the physical interpretation of the phenomenon under analysis: (i) Estimation of the probability density of multivariate data without physical meaning; (ii) learning of some bases (usually called activation functions), which are more or less connected to the actual behaviors that are implicit in the physical phenomenon; and (iii) to identify where sources are originated and how they mix before arriving to the sensors to provide a physical explanation of the linear mixture model. In any case, even though the complexity of the problem constrains a physical interpretation, ICA can be used as a general-purpose data mining technique. The chapters that compose this book are written by premier researchers that present enlightening discussions, convincing demonstrations, and guidelines for future directions of research. The contents of this book span biomedical signal processing, dynamic modeling, next generation wireless communication, and sound and ultrasound signal processing. It also includes comprehensive works based on the related ICA techniques known as bounded component analysis (BCA) and non-negative matrix factorization"--
Membrane Potential Imaging In The Nervous System And Heart
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Author : Marco Canepari
language : en
Publisher: Springer
Release Date : 2015-08-03
Membrane Potential Imaging In The Nervous System And Heart written by Marco Canepari and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-03 with Medical categories.
This volume discusses membrane potential imaging in the nervous system and in the heart and modern optical recording technology. Additionally, it covers organic and genetically-encoded voltage-sensitive dyes; membrane potential imaging from individual neurons, brain slices, and brains in vivo; optical imaging of cardiac tissue and arrhythmias; bio-photonics modelling. This is an expanded and fully-updated second edition, reflecting all the recent advances in this field. Twenty chapters, all authored by leading names in the field, are cohesively structured into four sections. The opening section focuses on the history and principles of membrane potential imaging and lends context to the following sections, which examine applications in single neurons, networks, large neuronal populations and the heart. Topics discussed include population membrane potential signals in development of the vertebrate nervous system, use of membrane potential imaging from dendrites and axons, and depth-resolved optical imaging of cardiac activation and repolarization. The final section discusses the potential – and limitations – for new developments in the field, including new technology such as non-linear optics, advanced microscope designs and genetically encoded voltage sensors. Membrane Potential Imaging in the Nervous System and Heart is ideal for neurologists, electro physiologists, cardiologists and those who are interested in the applications and the future of membrane potential imaging.
Machine Learning Algorithms For Problem Solving In Computational Applications Intelligent Techniques
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Author : Kulkarni, Siddhivinayak
language : en
Publisher: IGI Global
Release Date : 2012-06-30
Machine Learning Algorithms For Problem Solving In Computational Applications Intelligent Techniques written by Kulkarni, Siddhivinayak and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-30 with Computers categories.
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.