TY - JOUR
T1 - Sparse blind source separation via ℓ1-norm optimization
AU - Georgiou, Tryphon T.
AU - Tannenbaum, Allen
PY - 2010
Y1 - 2010
N2 - The title of the paper refers to an extension of the classical blind source separation where the mixing of unknown sources is assumed in the form of convolution with impulse response of unknown linear dynamics. A further key assumption of our approach is that source signals are considered to be sparse with respect to a known dictionary, and thereby, an ℓ1- optimization is a natural formalism for solving the un-mixing problem.We demonstrate the effectiveness of the framework numerically.
AB - The title of the paper refers to an extension of the classical blind source separation where the mixing of unknown sources is assumed in the form of convolution with impulse response of unknown linear dynamics. A further key assumption of our approach is that source signals are considered to be sparse with respect to a known dictionary, and thereby, an ℓ1- optimization is a natural formalism for solving the un-mixing problem.We demonstrate the effectiveness of the framework numerically.
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U2 - 10.1007/978-3-540-93918-4_29
DO - 10.1007/978-3-540-93918-4_29
M3 - Article
AN - SCOPUS:77950291206
SN - 0170-8643
VL - 398
SP - 321
EP - 330
JO - Lecture Notes in Control and Information Sciences
JF - Lecture Notes in Control and Information Sciences
ER -