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Separation of Dependent Autoregressive Sources Using Joint Matrix Diagonalization

Abstract : This letter proposes a novel technique for the blind separation of autoregressive (AR) sources. The latter relies on the joint diagonalization (JD) of appropriate AR matrix coefficients of the observed signals and can be applied to the separation of statistically dependent sources. The developed algorithm is referred to as ’DARSS-JD’ (for Dependent AR Source Separation using JD). Through the simulation experiments, DARSS-JD is shown to overcome existing second order separation methods with a relatively moderate computational cost.
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Contributor : Karim Abed-Meraim Connect in order to contact the contributor
Submitted on : Tuesday, January 13, 2015 - 9:35:03 PM
Last modification on : Saturday, June 25, 2022 - 10:12:42 AM


  • HAL Id : hal-01103038, version 1



Abdelwaheb Boudjellal, Mesloub Ammar, Karim Abed-Meraim, Adel Belouchrani. Separation of Dependent Autoregressive Sources Using Joint Matrix Diagonalization. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2014, pp.4. ⟨hal-01103038⟩



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