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Blind Source Separation: Advances in Theory, Algorithms and Applications Softcover reprint of the original 1st ed. 2014 [Pehme köide]

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  • Formaat: Paperback / softback, 551 pages, kõrgus x laius: 235x155 mm, kaal: 8424 g, 189 Illustrations, black and white; IX, 551 p. 189 illus., 1 Paperback / softback
  • Sari: Signals and Communication Technology
  • Ilmumisaeg: 20-Sep-2016
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662514036
  • ISBN-13: 9783662514030
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Blind Source Separation: Advances in Theory, Algorithms and Applications Softcover reprint of the original 1st ed. 2014Suurem pilt
  • Formaat: Paperback / softback, 551 pages, kõrgus x laius: 235x155 mm, kaal: 8424 g, 189 Illustrations, black and white; IX, 551 p. 189 illus., 1 Paperback / softback
  • Sari: Signals and Communication Technology
  • Ilmumisaeg: 20-Sep-2016
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662514036
  • ISBN-13: 9783662514030
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783662514030.html
Märksõnad:
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.

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.

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.