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Audio Source Separation

  • Shoji Makino

Part of the Signals and Communication Technology book series (SCT)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Cédric Févotte, Emmanuel Vincent, Alexey Ozerov
    Pages 1-24
  3. Paris Smaragdis, Gautham Mysore, Nasser Mohammadiha
    Pages 49-71
  4. Alexey Ozerov, Cédric Févotte, Emmanuel Vincent
    Pages 73-94
  5. Hirokazu Kameoka, Hiroshi Sawada, Takuya Higuchi
    Pages 95-124
  6. Daichi Kitamura, Nobutaka Ono, Hiroshi Sawada, Hirokazu Kameoka, Hiroshi Saruwatari
    Pages 125-155
  7. Aditya Arie Nugraha, Antoine Liutkus, Emmanuel Vincent
    Pages 157-185
  8. Minje Kim, Paris Smaragdis
    Pages 187-206
  9. Jitong Chen, DeLiang Wang
    Pages 207-235
  10. Hendrik Barfuss, Klaus Reindl, Walter Kellermann
    Pages 237-278
  11. Nobutaka Ito, Shoko Araki, Tomohiro Nakatani
    Pages 279-300
  12. Shmulik Markovich-Golan, Israel Cohen, Sharon Gannot
    Pages 301-331
  13. David Dov, Ronen Talmon, Israel Cohen
    Pages 365-382
  14. Back Matter
    Pages 383-385

About this book

Introduction

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.

Keywords

audio source separation methods non-negative matrix factorization (NMF) deep neural networks (DNN) for source separation sparse component analysis (SCA) non-negative spectrogram factorization multi-channel source separation audio directional statistics modelling DNN based mask estimation monaural speech separation multi-microphone MMSE-based techniques

Editors and affiliations

  • Shoji Makino
    • 1
  1. 1.University of TsukubaIbarakiJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-73031-8
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-73030-1
  • Online ISBN 978-3-319-73031-8
  • Series Print ISSN 1860-4862
  • Series Online ISSN 1860-4870
  • Buy this book on publisher's site
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