INVESTIGATING THE LIMITS OF THE AUTOREGRESSIVE MODEL for 1D and 2D SIGNAL PROCESSING TECHNIQUES

Clarysse Ramananjarasoa , Eric Grivel (1) , Mohamed Najim (1)

1 IMS - Laboratoire de l'intégration, du matériau au système

Even if the Autoregressive (AR) model is widely used in several frameworks such as speech, seismic and biomedical or textured image analysis, it is not really efficient to represent periodical signals. In this paper we propose first to investigate the limits of the autoregressive process and secondly to present an alternative approach based on the Wold decomposition. We then present a comparative study between AR model and Wold decomposition in the framework of the reconstruction of textured images

Mots clés: autoregressive process; sinusoidal model; Wold decomposition; speech; sleep spindles; texture characterization

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Clarysse Ramananjarasoa, Eric Grivel, Mohamed Najim. INVESTIGATING THE LIMITS OF THE AUTOREGRESSIVE MODEL for 1D and 2D SIGNAL PROCESSING TECHNIQUES. JTEA, 2002, Sousse, Tunisia. pp. ⟨hal-00167774⟩