Source Separation and Machine Learning highlights 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 which is seen as a statistical model for a whole system.
Looking at different models such as independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), this book addresses how these have evolved to deal with multichannel source separation and single-channel source separation, and explains the weaknesses and the strengths of these models in different mixing conditions.
- The first book to emphasize 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 all in one book
- 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
Part I Fundamental Theories 1. Introduction 2. Model-based blind source separation 3. Adaptive learning machines
Part II Advanced Studies 4. Independent component analysis 5. Nonnegative matrix factorization 6. Nonnegative tensor factorization 7. Deep neural network
Jen-Tzung Chien received his Ph.D. degree in electrical engineering from National Tsing Hua University, Hsinchu, Taiwan, ROC, in 1997. During 1997-2012, he was with the National Cheng Kung University, Tainan, Taiwan. He has been with the Department of Electrical and Computer Engineering, National Chiao Tung University (NCTU), Hsinchu since 2012. He currently serves as an adjunct professor in the Department of Computer Science, NCTU.
He has held Visiting Professor Positions at the Panasonic Technologies Inc., Santa Barbara, CA, the Tokyo Institute of Technology, Japan, the Georgia Institute of Technology, Atlanta, GA, the Microsoft Research Asia, Beijing, China, and the IBM T. J. Watson Research Center, Yorktown Heights, NY.
Dr. Chien served as the associate editor of the IEEE Signal Processing Letters in 2008-2011 and the tutorial speaker of the ICASSP, in 2012, the INTERSPEECH, in 2013, the APSIPA, in 2013, and the ISCSLP, in 2014. He received the Distinguished Research Awards from the Ministry of Science and Technology, Tawian and the Best Paper Award of the IEEE Automatic Speech Recognition and Understanding Workshop in 2011. He is currently serving as an elected member of the IEEE Machine Learning for Signal Processing Technical Committee.