אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Blind Source Separation (BSS) is a very applicable and well-studied problem. Most studies of the BSS problem assume the system to be time/position invariant, an assumption which assists the mathematical study, but is not guaranteed for real world situations. We present a method applicable to the case of underdetermined time/position varying mixing systems, where number of mixture observation is limited to be less than the number of sources and the system is changing with time/position according to a parametric model. Our method is based on Staged Sparse Component Analysis (SSCA), which uses signal sparseness to estimate the varying system and then inverse it to allow source estimation. A variation called

Underdetermined SSCA (UDSSCA) is defined, which uses the signal sparseness for signal estimation instead of mixing system inversion. The process of signal estimation is performed in a sparse domain by the use of a winner-takes-all heuristic strategy inspired by minimization of l1 norm