Blind Identification And Separation Of Sources With Sparse Events
Bahador Makki Abadi, Saeid Sanei

In this paper, a new tensor factorization method
based on k-SCA [1] approach is developed to solve the underdetermined
blind identification (UBI) problem where k sources
are active in each signal segment. Similar to k-SCA methods
we assume our k is equal to the number of sensors minus one.
This approach improves the general upper bound for maximum
possible number of sources in a second order underdetermined
blind identification method called SOBIUM. The method is
applied to the mixtures of synthetic signals and the results
are illustrated. Compared to the recently developed SOBIUM
approach, the proposed method is able to identify the channels
for more number of source signals. Using the estimated mixing
channels the separation of sources is also easily possible.