TY - GEN
T1 - Separation of periodically time-varying mixtures using second-order statistics
AU - Weisman, Tzahi
AU - Yeredor, Arie
PY - 2006
Y1 - 2006
N2 - We address the problem of Blind Source Separation (BSS) in the context of instantaneous (memoryless) linear mixtures, where the unknown mixing coefficients are time varying, changing periodically in time. Such a mixing model is realistic, e.g., when considering a biological or physiological system where the mixing coefficients are affected by periodic processes like breathing, heart-beating etc. Assuming stationary sources with distinct spectra, we rely on second-order statistics (SOS) and offer an expansion of the classical Second Order Blind Identification (SOBI) algorithm, accommodating the periodic variation model. The proposed algorithm consists of estimating several types of correlation matrices related to the time-varying SOS of the observations, followed by applying generalized joint diagonalization, which leads to estimates of the parameters of the periodic mixing. These estimated parameters are used in turn to apply a time-vary ing unmixing operation, recovering the desired sources. In its basic form (as presented in here), the algorithm requires prior knowledge (or a good estimate) of the cyclic period. We demonstrate the performance improvement over SOBI in simulation.
AB - We address the problem of Blind Source Separation (BSS) in the context of instantaneous (memoryless) linear mixtures, where the unknown mixing coefficients are time varying, changing periodically in time. Such a mixing model is realistic, e.g., when considering a biological or physiological system where the mixing coefficients are affected by periodic processes like breathing, heart-beating etc. Assuming stationary sources with distinct spectra, we rely on second-order statistics (SOS) and offer an expansion of the classical Second Order Blind Identification (SOBI) algorithm, accommodating the periodic variation model. The proposed algorithm consists of estimating several types of correlation matrices related to the time-varying SOS of the observations, followed by applying generalized joint diagonalization, which leads to estimates of the parameters of the periodic mixing. These estimated parameters are used in turn to apply a time-vary ing unmixing operation, recovering the desired sources. In its basic form (as presented in here), the algorithm requires prior knowledge (or a good estimate) of the cyclic period. We demonstrate the performance improvement over SOBI in simulation.
UR - http://www.scopus.com/inward/record.url?scp=33745683261&partnerID=8YFLogxK
U2 - 10.1007/11679363_35
DO - 10.1007/11679363_35
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:33745683261
SN - 3540326308
SN - 9783540326304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 278
EP - 285
BT - Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings
T2 - 6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006
Y2 - 5 March 2006 through 8 March 2006
ER -