Blind Source Separation

<p>Section 1: Theory, Algorithms and Extensions<br>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.- <br>Section 2: Applications <br>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.</p>