Source Separation and Machine Learning

Source Separation and Machine Learning
Authors: Jen-Tzung Chien
ISBN-10: 0128177969
ISBN-13: 9780128177969
Edition 版次: 1
Publication Date 出版日期: 2018-11-06
Print Length 页数: 384 pages
5
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
List of Tables
Foreword
Preface
Acknowledgments
Notations and Abbreviations
Part 1: Fundamental Theories
1Introduction
2Model-Based Source Separation
3Adaptive Learning Machine
Part 2: Advanced Studies
4Independent Component Analysis
5Nonnegative Matrix Factorization
6Nonnegative Tensor Factorization
7 Deep Neural Network
8 Summary and Future Trends
APPENDIX A Basic Formulas
APPENDIX B Probabilistic Distribution Functions
Bibliography

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