Skip to main content

About this book

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.

Table of Contents


2018 | OriginalPaper | Chapter

Chapter 1. Single-Channel Audio Source Separation with NMF: Divergences, Constraints and Algorithms

Cédric Févotte, Emmanuel Vincent, Alexey Ozerov

2018 | OriginalPaper | Chapter

Chapter 2. Separation of Known Sources Using Non-negative Spectrogram Factorisation

Tuomas Virtanen, Tom Barker

2018 | OriginalPaper | Chapter

Chapter 3. Dynamic Non-negative Models for Audio Source Separation

Paris Smaragdis, Gautham Mysore, Nasser Mohammadiha

2018 | OriginalPaper | Chapter

Chapter 4. An Introduction to Multichannel NMF for Audio Source Separation

Alexey Ozerov, Cédric Févotte, Emmanuel Vincent

2018 | OriginalPaper | Chapter

Chapter 5. General Formulation of Multichannel Extensions of NMF Variants

Hirokazu Kameoka, Hiroshi Sawada, Takuya Higuchi

2018 | OriginalPaper | Chapter

Chapter 6. Determined Blind Source Separation with Independent Low-Rank Matrix Analysis

Daichi Kitamura, Nobutaka Ono, Hiroshi Sawada, Hirokazu Kameoka, Hiroshi Saruwatari

2018 | OriginalPaper | Chapter

Chapter 7. Deep Neural Network Based Multichannel Audio Source Separation

Aditya Arie Nugraha, Antoine Liutkus, Emmanuel Vincent

2018 | OriginalPaper | Chapter

Chapter 8. Efficient Source Separation Using Bitwise Neural Networks

Minje Kim, Paris Smaragdis

2018 | OriginalPaper | Chapter

Chapter 9. DNN Based Mask Estimation for Supervised Speech Separation

Jitong Chen, DeLiang Wang

2018 | OriginalPaper | Chapter

Chapter 10. Informed Spatial Filtering Based on Constrained Independent Component Analysis

Hendrik Barfuss, Klaus Reindl, Walter Kellermann

2018 | OriginalPaper | Chapter

Chapter 11. Recent Advances in Multichannel Source Separation and Denoising Based on Source Sparseness

Nobutaka Ito, Shoko Araki, Tomohiro Nakatani

2018 | OriginalPaper | Chapter

Chapter 12. Multimicrophone MMSE-Based Speech Source Separation

Shmulik Markovich-Golan, Israel Cohen, Sharon Gannot

2018 | OriginalPaper | Chapter

Chapter 13. Musical-Noise-Free Blind Speech Extraction Based on Higher-Order Statistics Analysis

Hiroshi Saruwatari, Ryoichi Miyazaki

2018 | OriginalPaper | Chapter

Chapter 14. Audio-Visual Source Separation with Alternating Diffusion Maps

David Dov, Ronen Talmon, Israel Cohen


Additional information