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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.
List of contents
Section 1: Theory, Algorithms and Extensions
Quantum independent component analysis and related statistical blind qubit uncoupling methods.- Blind source separation based on dictionary learning: a singularity-aware approach.- Performance study for complex independent component analysis.- Sub-band based- blind source separation and permutation alignment.- Frequency domain blind source separation based on independent vector analysis with a multivariate Gaussian source prior.- Sparse component analysis: a general framework for linear or nonlinear blind unmixing of signals or images.- Underdetermined audio source separation using Laplacian mixture modelling.- Itakura-Saito nonnegative matrix two-dimensional factorizations for blind single channel audio separation.- Source localisation and tracking: a maximum a posterior based approach.-
Section 2: Applications
Statistical analysis and evaluation of blind speech extraction algorithms.- Speech separation and extraction by combining super directive beam forming and blind source separation.- On the ideal ratio mask as the goal of computational auditory scene analysis.- Monaural speech enhancement based on multi-threshold masking.- REPET for background/foreground separation.- Non-negative matrix factorization sparse coding strategy for cochlear implants.- Exploratory analysis of brain with ICA.- Supervised normalisation of large-scale omic datasets using blind source separation.- FebICA: feedback independent component analysis for complex domain source separation of communication signals.- Semi-blind functional source separation algorithm from non-invasive electrophysiology to neuroimaging.
Summary
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.
