Détection de signaux non stationnaires par un graphe de Markov local en temps et sélectif en fréquence

We deal in this paper with the extraction of multiresolution statistical signatures for the characterization of transient signals in strongly noisy contexts. These short-time signals have sharp and highly variable frequency components. The time/frequency window to chose for our analysis is then a major issue. We have chosen the Wavelet Packet Transform due to its ability to provide multiple windows analysis with different time/frequency resolutions. We propose a new oriented Hidden Markov Tree dedicated to the tree structure of the Wavelet Packet Transform, which offers promising statistical characterization of time/frequency variations in a signal, by exploiting several time/frequency resolutions. This model is exploited in a bayesian context for the segmentation of signals containing transient components. We demonstrate the efficiency of our method on synthetic signals with several Signal to Noise Ratio.