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Détection de signaux non stationnaires par un graphe de Markov local en temps et sélectif en fréquence
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cam, Steven Le Collet, Christophe Salzenstein, Fabien |
| Copyright Year | 2009 |
| Abstract | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://documents.irevues.inist.fr/bitstream/handle/2042/29059/lecam_365.pdf?sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
