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Summary: UNDERDETERMINED SPARSE BLIND SOURCE SEPARATION WITH DELAYS
Rayan Saab, ĻOzgĻur Yilmaz, Martin J. McKeown, Rafeef Abugharbieh
ABSTRACT
In this paper, we address the problem of under-determined blind
source separation (BSS), mainly for speech signals, in an anechoic
environment. Our approach is based on exploiting the sparsity of
Gabor expansions of speech signals. For parameter estimation, we
adopt the clustering approach of DUET [19]. However, unlike in the
case of DUET where only two mixtures are used, we use all avail-
able mixtures to get more precise estimates. For source extraction,
we propose two methods, both of which are based on constrained
optimization. Our first method uses a constrained q
(0 < q 1)
approach, and our second method uses a constrained "modified" 1
minimization approach. In both cases, our algorithms use all avail-
able mixtures, and are suited to the anechoic mixing scenario. Ex-
periments indicate that the performances of the proposed algorithms
are superior compared to DUET in many different settings.
1. INTRODUCTION
BSS algorithms can be categorized according to the assumptions
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