On the Ideal Ratio Mask as the Goal of Computational Auditory Scene Analysis
The ideal binary mask (IBM) is widely considered to be the benchmark for time–frequency-based sound source separation techniques such as computational auditory scene analysis (CASA). However, it is well known that binary masking introduces objectionable d
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Ganesh R. Naik Wenwu Wang Editors
Blind Source Separation Advances in Theory, Algorithms and Applications
Signals and Communication Technology
For further volumes: http://www.springer.com/series/4748
Ganesh R. Naik Wenwu Wang •
Editors
Blind Source Separation Advances in Theory, Algorithms and Applications
123
Editors Ganesh R. Naik University of Technology Sydney Australia
Wenwu Wang University of Surrey Guildford UK
ISSN 1860-4862 ISSN 1860-4870 (electronic) ISBN 978-3-642-55015-7 ISBN 978-3-642-55016-4 (eBook) DOI 10.1007/978-3-642-55016-4 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2014940320 Springer-Verlag Berlin Heidelberg 2014 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Blind source separation (BSS) methods have received extensive attention over the past two decades; thanks to its wide applicability in a number of areas such as biomedical engineering, audio signal processing, and telecommunications. The problem of source separation is an inductive inference problem, as only limited information, e.g., the sensor observations, is available to infer the most probable source estimates. The aim of BSS is to process these observations (acquired by sensors or sensor arrays) in such a way that the o
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