A seminar titled "Blind Source Separation with Binary Sources " was held in KAIST.
Title : Blind Source Separation with Binary Sources
Date : September 16 (Friday), 2011 14:00~16:00
Location : Wooribyul Seminar Room(#2201), Department of Electrical Engineering
Speaker : Prof. Konstantinos Diamantaras/ Department of Informatics, echnological Education Institute (T.E.I.) of Thessaloniki
Abstract :
We propose a new method for the blind separation of multiple binary signals from a single general nonlinear mixture. In addition to the usual independence assumption on the input signals our key hypothesis is the asymmetry of the source probabilities. This condition allows us to express the output probability distribution as a linear mixture of the sources. We then proceed to solve the problem using known linear BSS methods for the binary underdetermined case. The method is based on clustering avoiding costly iterative optimization. Our simulations demonstrate successful separation for up to four sources. The problem however grows exponentially with the number of sources n, and the dataset size required for accurate estimation may become prohibitively large for large n.