Orthogonal Segmented Model For Underdetermined Blind Identification And Separation Of Sources With Sparse Events
Bahador Makki Abadi, Saeid Sanei

In this paper, a novel tensor factorization method
based on ka-SCA (called k-SCA in [1]) approach is developed to
solve the underdetermined blind source separation (UBSS) and
especially underdetermined blind identification (UBI) problems
where ka sources are active in each signal segment. Similar
to ka-SCA methods we assume our ka is equal to, or less
than, the number of sensors minus one when sources are
mixed instantaneously. This approach improves the general upper
bound for maximum possible number of sources in the second
order underdetermind blind identification problem suggested
by well known tensor based methods. Alternating constrained
optimization approaches are developed to estimate the mixing
model and the rank deficient segments. Also this method provides
sub-optimum solutions to the UBSS problem. The method is
applied to mixtures of synthetic and real signals of sparse events
such as instantaneously mixed speech signals. The obtained
results show a marked improvement in separability (e.g. it can be
used for blind identification and separation of up to 10 speech
sources out of 3 sensors) and channel identification compared
with other well-established approaches.