Source Separation and Machine Learning
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About this ebook
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
Jen-Tzung Chien
Jen-Tzung Chien received his Ph.D. in electrical engineering from National Tsing Hua University, Taiwan in 1997. He is now with the Department of Electrical and Computer Engineering and the Department of Computer Science at the National Chiao Tung University, Taiwan, where he is currently the Chair Professor. He was the visiting professor at the IBM T. J. Watson Research Center, Yorktown Heights, NY in 2010. Dr. Chien has served as the associate editor of the IEEE Signal Processing Letters in 2008-2011, the tutorial speaker of the ICASSP in 2012, 2015, 2017, the INTERSPEECH in 2013, 2016, the COLING in 2018, and the general chair of the IEEE International Workshop on Machine Learning for Signal Processing in 2017. He received the Best Paper Award of the IEEE Automatic Speech Recognition and Understanding Workshop in 2011 and the AAPM Farrington Daniels Paper Award in 2018. He is currently serving as an elected member of the IEEE Machine Learning for Signal Processing Technical Committee.
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Book preview
Source Separation and Machine Learning - Jen-Tzung Chien
314)
Part 1
Fundamental Theories
Outline
Chapter 1. Introduction
Chapter 2. Model-Based Source Separation
Chapter 3. Adaptive Learning Machine
Chapter 1
Introduction
Abstract
This chapter provides a brief introduction to the fundamentals and advances in machine learning approaches for blind source separation. The basics of learning algorithms and separation models are introduced. We then point out different challenges and problems which front-end signal processing or back-end machine learning methods can resolve. A variety of source separation systems and applications are addressed. The trends of recent works on source separation are included. The impacts of deep learning in source separation are emphasized. The overview of the organization of this book is described.
Keywords
source separation; machine learning; signal processing; deep learning
In real world, mixed signals are received everywhere. The observations perceived by a human are degraded. It is difficult to acquire faithful information from environments in many cases. For example, we are surrounded by sounds and noises with interference from room reverberation. Multiple sources are active simultaneously. The sound effects or listening conditions for speech and audio signals are considerably deteriorated. From the perspective of computer vision, an observed image is usually blurred by noise, illuminated by lighting or mixed with the other image due to reflection. The target object becomes hard to detect and recognize. In addition, it is also important to deal with the mixed signals of medical imaging data, including magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). The mixing interference from external sources of electromagnetic fields due to the muscle activity significantly masks the desired measurement from brain activity. Therefore, how to come up with a powerful solution to separate a mixed signal into its individual source signals is nowadays a challenging problem, which has attracted many researchers working in this direction and developing practical systems and