Blind signal separation and Independent component analysis

Course Description

Mathematical preliminaries: stochastic processes, gradients and optimization methods, information theory. Blind separation of signals by principal (PCA) and independen (ICA) component analysis:conditions for uniqueness of the solution. Information-theoretic approach to ICA. Equivalence between ICA methods based on minimum of mutual information and maximum negentropy. Single and multichannel blind deconvolution in timr and frequency domains. Underdetermined blind source separation: sparse component analysis and nonnegative matrix factorizations.

Study Programmes

Grading System

ID 154771