"Independent Component Analysis for Audio and Biosignal Applications" ed. by Ganesh R Naik
InTeOp | 2012 | ISBN: 9535107828 9789535107828 | 354 pages | PDF 

This book brings the state-of-the-art of some of the most important current research of Independent Component Analysis (ICA) related to Audio and Biomedical signal processing applications.

The book is partly a textbook and partly a monograph. It is a textbook because it gives a detailed introduction to ICA applications. It is simultaneously a monograph because it presents several new results, concepts and further developments, which are brought together and published in the book.

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.

Contents
Preface
Section 1 Introduction
1 Introduction: Independent Component Analysis
Section 2 ICA: Audio Applications
2 On Temporomandibular Joint Sound Signal Analysis Using ICA
3 Blind Source Separation for Speech Application Under Real Acoustic Environment
4 Monaural Audio Separation Using Spectral Template and Isolated Note Information
5 Non-Negative Matrix Factorization with Sparsity Learning for Single Channel Audio Source Separation
6 Unsupervised and Neural Hybrid Techniques for Audio Signal Classification
7 Convolutive ICA for Audio Signals
Section 3 ICA: Biomedical Applications
8 Nonlinear Independent Component Analysis for EEG-Based Brain-Computer Interface Systems
9 Associative Memory Model Based in ICA Approach to Human Faces Recognition
10 Application of Polynomial Spline Independent Component Analysis to fMRI Data
11 Preservation of Localization Cues in BSS-Based Noise Reduction: Application in Binaural Hearing Aids
12 ICA Applied to VSD Imaging of Invertebrate Neuronal Networks
13 ICA-Based Fetal Monitoring
Section 4 ICA: Time-Frequency Analysis
14 Advancements in the Time-Frequency Approach to Multichannel Blind Source Separation
15 A Study of Methods for Initialization and Permutation Alignment for Time-Frequency Domain Blind Source Separation
16 Blind Implicit Source Separation - A New Concept in BSS Theory