INDEPENDENT COMPONENT ANALYSIS FOR AUDIO AND BIOSIGNAL APPLICATIONS Edited by Ganesh R. Naik Independent Component Analysis for Audio and Biosignal Applications Edited by Ganesh R. Naik Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Iva Lipovic Technical Editor Teodora Smiljanic Cover Designer InTech Design Team First published October, 2012 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechopen.com Independent Component Analysis for Audio and Biosignal Applications, Edited by Ganesh R. Naik p. cm. ISBN 978-953-51-0782-8 Contents Preface IX Section 1 Introduction 1 Chapter 1 Introduction: Independent Component Analysis 3 Ganesh R. Naik Section 2 ICA: Audio Applications 23 Chapter 2 On Temporomandibular Joint Sound Signal Analysis Using ICA 25 Feng Jin and Farook Sattar Chapter 3 Blind Source Separation for Speech Application Under Real Acoustic Environment 41 Hiroshi Saruwatari and Yu Takahashi Chapter 4 Monaural Audio Separation Using Spectral Template and Isolated Note Information 67 Anil Lal and Wenwu Wang Chapter 5 Non-Negative Matrix Factorization with Sparsity Learning for Single Channel Audio Source Separation 91 Bin Gao and W.L. Woo Chapter 6 Unsupervised and Neural Hybrid Techniques for Audio Signal Classification 117 Andrés Ortiz, Lorenzo J. Tardón, Ana M. Barbancho and Isabel Barbancho Chapter 7 Convolutive ICA for Audio Signals 137 Masoud Geravanchizadeh and Masoumeh Hesam Section 3 ICA: Biomedical Applications 163 Chapter 8 Nonlinear Independent Component Analysis for EEG-Based Brain-Computer Interface Systems 165 Farid Oveisi, Shahrzad Oveisi, Abbas Efranian and Ioannis Patras VI Contents Chapter 9 Associative Memory Model Based in ICA Approach to Human Faces Recognition 181 Celso Hilario, Josue-Rafael Montes, Teresa Hernández, Leonardo Barriga and Hugo Jiménez Chapter 10 Application of Polynomial Spline Independent Component Analysis to fMRI Data 197 Atsushi Kawaguchi, Young K. Truong and Xuemei Huang Chapter 11 Preservation of Localization Cues in BSS-Based Noise Reduction: Application in Binaural Hearing Aids 209 Jorge I. Marin-Hurtado and David V. Anderson Chapter 12 ICA Applied to VSD Imaging of Invertebrate Neuronal Networks 235 Evan S. Hill, Angela M. Bruno, Sunil K. Vasireddi and William N. Frost Chapter 13 ICA-Based Fetal Monitoring 247 Rubén Martín-Clemente and José Luis Camargo-Olivares Section 4 ICA: Time-Frequency Analysis 269 Chapter 14 Advancements in the Time-Frequency Approach to Multichannel Blind Source Separation 271 Ingrid Jafari, Roberto Togneri and Sven Nordholm Chapter 15 A Study of Methods for Initialization and Permutation Alignment for Time-Frequency Domain Blind Source Separation 297 Auxiliadora Sarmiento, Iván Durán, Pablo Aguilera and Sergio Cruces Chapter 16 Blind Implicit Source Separation – A New Concept in BSS Theory 321 Fernando J. Mato-Méndez and Manuel A. Sobreira-Seoane Preface Background and Motivation Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image processing, telecommunications, medical signal processing and several data mining issues. This book presents theories and applications of ICA related to Audio and Biomedical signal processing applications and include invaluable examples of several real-world applications. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, and cumulant-based techniques are reviewed and put in an information theoretic framework to merge several lines of ICA research. The ICA algorithm has been successfully applied to many biomedical signal- processing problems such as the analysis of Electromyography (EMG), Electroencephalographic (EEG) data and functional Magnetic Resonance Imaging (fMRI) data. The ICA algorithm can furthermore be embedded in an expectation maximization framework for unsupervised classification. It is also abundantly clear that ICA has been embraced by a number of researchers involved in Biomedical Signal processing as a powerful tool, which in many applications has supplanted decomposition methods such as Singular Value Decomposition (SVD). The book provides wide coverage of adaptive BSS techniques and algorithms both from the theoretical and practical point of view. The main objective is to derive and present efficient and simple adaptive algorithms that work well in practice for real-world Audio and Biomedical data. This book is aimed to provide a self-contained introduction to the subject as well as offering a set of invited contributions, which we see as lying at the cutting edge of ICA research. ICA is intimately linked with the problem of Blind Source Separation (BSS) – attempting to recover a set of underlying sources when only a mapping from these sources, the observations, is given - and we regard this as canonical form of ICA. This book was created from discussions with researchers in the ICA community and aims to provide a snapshot of some current trends in ICA research. X Preface Intended Readership This book brings the state-of-the-art of Audio and Biomedical signal research related to BSS and ICA. The book is partly a textbook and partly a monograph. It is a textbook because it gives a detailed introduction to BSS/ICA techniques and applications. It is simultaneously a monograph because it presents several new results, concepts and further developments that are brought together and published in the book. It is essential reading for researchers and practitioners with an interest in ICA. Furthermore, the research results previously scattered in many scientific journals and conference papers worldwide are methodically collected and presented in the book in a unified form. As a result of its dual nature the book is likely to be of interest to graduate and postgraduate students, engineers and scientists - in the field of signal processing and biomedical engineering. This book can also be used as handbook for students and professionals seeking to gain a better understanding of where Audio and Biomedical applications of ICA/BSS stand today. One can read this book through sequentially but it is not necessary since each chapter is essentially self-contained, with as few cross-references as possible. So, browsing is encouraged. This book is organized into 16 chapters, covering the current theoretical approaches of ICA, especially Audio and Biomedical Engineering, and applications. Although these chapters can be read almost independently, they share the same notations and the same subject index. Moreover, numerous cross-references link the chapters to each other. As an Editor and also an Author in this field, I am privileged to be editing a book with such intriguing and exciting content, written by a selected group of talented researchers. I would like to thank the authors, who have committed so much effort to the publication of this work. Dr. Ganesh R. Naik RMIT University, Melbourne, Australia . INDEPENDENT COMPONENT ANALYSIS FOR AUDIO AND BIOSIGNAL APPLICATIONS Edited by Ganesh R. Naik Independent Component Analysis for Audio and Biosignal Applications. ignores all noise components and any time delay in the recordings. 4 Independent Component Analysis for Audio and Biosignal Applications Introduction: Independent Component Analysis 3 Fig. 1 that kurt (s 1 ±s 2 )=kurt(s 1 ) ±kurt(s 2 ) (8) 6 Independent Component Analysis for Audio and Biosignal Applications Introduction: Independent Component Analysis 5 and kurt (αs 1 )=α 4 kurt(s 1 ) (9) where α is a