Source Separation And Machine Learning
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Source Separation And Machine Learning
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Author : Jen-Tzung Chien
language : en
Publisher: Academic Press
Release Date : 2018-11-01
Source Separation And Machine Learning written by Jen-Tzung Chien and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-01 with Technology & Engineering categories.
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
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Author : Emmanuel Vincent
language : en
Publisher: John Wiley & Sons
Release Date : 2018-07-24
Audio Source Separation And Speech Enhancement written by Emmanuel Vincent and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-24 with Technology & Engineering categories.
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
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Author : In Tae Lee
language : en
Publisher:
Release Date : 2009
Machine Learning Algorithms For Independent Vector Analysis And Blind Source Separation written by In Tae Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
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
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Author : João Marcos Travassos Romano
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Unsupervised Signal Processing written by João Marcos Travassos Romano and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Computers categories.
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.
Handbook Of Blind Source Separation
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Author : Pierre Comon
language : en
Publisher: Academic Press
Release Date : 2010-02-17
Handbook Of Blind Source Separation written by Pierre Comon and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-17 with Technology & Engineering categories.
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
Nonlinear Source Separation
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Author : Luis B. Almeida
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2006
Nonlinear Source Separation written by Luis B. Almeida and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Blind source separation categories.
"The purpose of this lecture book is to present the state of the art in nonlinear blind source separation, in a form appropriate for students, researchers and developers. The author reviews the main nonlinear separation methods, including the separation of post-nonlinear mixtures, and the MISEP, ensemble learning and kTDSEP methods for generic mixtures. These methods are studied with a significant depth. A historical overview is also presented, mentioning most of the relevant results, on nonlinear blind source separation, that have been presented over the years."--BOOK JACKET.
Nonlinear Blind Source Separation And Blind Mixture Identification
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Author : Yannick Deville
language : en
Publisher: Springer Nature
Release Date : 2021-02-02
Nonlinear Blind Source Separation And Blind Mixture Identification written by Yannick Deville and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-02 with Technology & Engineering categories.
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.
Audio Source Separation Using Wavenet Architecture With Wavelet Transformed Audio As Input
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Author : Prathmesh Ravindra Matodkar
language : en
Publisher:
Release Date : 2019
Audio Source Separation Using Wavenet Architecture With Wavelet Transformed Audio As Input written by Prathmesh Ravindra Matodkar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Computer sound processing categories.
Audio Source Separation is an interesting problem, which gives us the power to separate individual elements that make up a mixture signal and analyze them or use them or different functions ranging from re mixing, mastering or for educational purpose.With different instruments, sounds, timbers interacting with each other, it is difficult to visualize their combination to make the final mixture signal.There were few methods which attempted exploiting the statistical relations of the individual sources with final the final mixture signals.With the arrival of machine learning, neural networks, researchers are curious to know the outcome of applying various deep learning models for solving this problem of audio source separation. The availability of larger memory and processing power has encouraged the use of deep learning methodologies in solving various problems.Their ability find interesting patterns with the introduction of non linearity, convolutions layers, short memory cells has helped achieve better results in the domains of image, video, audio. These models are flexible, hence a model used in one domain can be modified to suite other domains as well. The development of various APIs like Tensorflow, Keras, Theano, Pytorch has made the realization and application of complicated operations involved in deep learning models easy to understand and implement. A song is made up of different sources, instruments. In this thesis our main focus would be to extract bass, drums and vocals from a given song.These three elemnts have distinct timber and also different frequency regions where they have maximum presence.These sources are also the driving force of a song. Different techniques have been used till date to solve this problem.An overview of these techniques, proposed model and the elements included are explained in the chapters ahead.
Information Retrieval From Marine Soundscape By Using Machine Learning Based Source Separation
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Author : Tzu-Hao Lin
language : en
Publisher:
Release Date : 2018
Information Retrieval From Marine Soundscape By Using Machine Learning Based Source Separation written by Tzu-Hao Lin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
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.
