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
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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

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




Section 1
Introduction