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Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms and applications of BSS.

Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.

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Theory.- BSS algorithms using Second order and Higher Order statistics.- Sparse BSS methods.- Convolutive BSS.- Source Localisation.- Under complete BSS.- Over complete BSS.- Semi blind BSS methods.- Source Separation and Identification issues.- Unknown number of source separation using BSS.- Application.- BSS for Image processing applications.- Biomedical application of BSS.- BSS applications of Electromyography (EMG).- BSS applications of Electroencephalography (EEG).- BSS applications of Electrocardiography (ECG).- Artefact removal of biomedical data using BSS.- Source localisation of Audio and Bio signals.- Source separation and identification issued in Audio and Bio signals.- Over complete BSS for Audio and Bio signals.- Under complete BSS for Audio and Bio signals.- BSS for Music separation.- Source separation in retinal and MRI imaging applications.- Semi blind BSS for Audio and Biomedical data.- Analysis of Heart rate analysis using BSS.- Comparison of real word audio and bio signals with synthetic data using BSS.- BSS for financial and economics applications.- BSS for moving source separation.

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