Springer Audio Source Separation (Signals and Communication Technology)

This book provides the first comprehensive overview of the fascinating topic of audio source separation based on non-negative matrix factorization, deep neural networks, and sparse component analysis. The first section of the book covers single channel source separation based on non-negative matrix factorization (NMF). After an introduction to the technique, two further chapters describe separation of known sources using non-negative spectrogram factorization, and temporal NMF models. In section two, NMF methods are extended to multi-channel source separation. Section three introduces deep neural network (DNN) techniques, with chapters on multichannel and single channel separation, and a further chapter on DNN based mask estimation for monaural speech separation. In section four, sparse component analysis (SCA) is discussed, with chapters on source separation using audio directional statistics modelling, multi-microphone MMSE-based techniques and diffusion map methods. The book brings together leading researchers to provide tutorial-like and in-depth treatments on major audio source separation topics, with the objective of becoming the definitive source for a comprehensive, authoritative, and accessible treatment. This book is written for graduate students and researchers who are interested in audio source separation techniques based on NMF, DNN and SCA. From the Back Cover This book provides the first comprehensive overview of the fascinating topic of audio source separation based on non-negative matrix factorization, deep neural networks, and sparse component analysis. The first section of the book covers single channel source separation based on non-negative matrix factorization (NMF). After an introduction to the technique, two further chapters describe separation of known sources using non-negative spectrogram factorization, and temporal NMF models. In section two, NMF methods are extended to multi-channel source separation. Section three introduces deep neural network (DNN) techniques, with chapters on multichannel and single channel separation, and a further chapter on DNN based mask estimation for monaural speech separation. In section four, sparse component analysis (SCA) is discussed, with chapters on source separation using audio directional statistics modelling, multi-microphone MMSE-based techniques and diffusion map methods. The book brings together leading researchers to provide tutorial-like and in-depth treatments on major audio source separation topics, with the objective of becoming the definitive source for a comprehensive, authoritative, and accessible treatment. This book is written for graduate students and researchers who are interested in audio source separation techniques based on NMF, DNN and SCA. About the Author SHOJI MAKINO (F) received the B. E., M. E., and Ph.D. degrees from Tohoku University, Japan, in 1979, 1981, and 1993, respectively. He joined NTT in 1981. He is now a Professor at University of Tsukuba. His research interests include adaptive filtering technologies, realization of acoustic echo cancellation, blind source separation of convolutive mixtures of speech, and acoustic signal processing for speech and audio applications. Dr. Makino received the IEEE SPS Best Paper Award in 2014, the IEEE MLSP Competition Award in 2007, the ICA Unsupervised Learning Pioneer Award in 2006, the Commendation for Science and Technology of Japanese Government in 2015, the TELECOM System Technology Award in 2015 and 2004, the Achievement Award of the Institute of Electronics, Information, and Communication Engineers (IEICE) in 1997, and the Outstanding Technological Development Award of the Acoustical Society of Japan (ASJ) in 1995, the Paper Award of the IEICE in 2005 and 2002, the Paper Award of the ASJ in 2005 and 2002. He is the author or co-author of more than 200 articles in journals and conference proceedings and is responsible for more than 1