Effet d'une perturbation sur l'estimation de modèles autorégressifs

Abstract
In spectral analysis and pattern recognition, especially in speech processing, AutoRegressive (AR) modeling is widely used as a signal parameter estimation tool. In this paper, AR estimation behaviour is studied when applied to signals presenting abrupt changes at an unknown instant of time. A particular abrupt change is studied : the additive change case. This change occurs when, at the instant of change, a signal adds to one which was present before - both signals are independent -. The theoretical sliding window AR parameter expressions are given. We show that blind sliding window AR estimation can lead to a detection tool, allowing additive changes to be detected and distinguished.