Title :
Separation of non stationary sources; achievable performance
Author :
Cardoso, Jean François
Author_Institution :
Dept. TSI, CNRS, Paris, France
Abstract :
We consider the blind separation of an instantaneous mixture of non-stationary source signals, possibly normally distributed. The asymptotic Cramer-Rao bound is exhibited in the case of known source distributions: it reveals how non-stationarity and non-Gaussianity jointly governs the achievable performance via an index of non-stationarity and an index of non-Gaussianity
Keywords :
normal distribution; signal processing; asymptotic Cramer-Rao bound; blind separation; instantaneous mixture; non-Gaussianity index; non-stationarity index; normal distribution; performance; source separation; Artificial intelligence; Covariance matrix; Eigenvalues and eigenfunctions; Equations; Gaussian distribution; Gaussian processes; Signal processing; Source separation; Upper bound; Yield estimation;
Conference_Titel :
Statistical Signal and Array Processing, 2000. Proceedings of the Tenth IEEE Workshop on
Conference_Location :
Pocono Manor, PA
Print_ISBN :
0-7803-5988-7
DOI :
10.1109/SSAP.2000.870144
