Time-Frequency Learning Machines For NonStationarity Detection Using Surrogates

Hassan Amoud (1) , Paul Honeine (1) , Cédric Richard (1) , Pierre Borgnat (2) , Patrick Flandrin (2)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 Phys-ENS - Laboratoire de Physique de l'ENS Lyon

Testing stationarity is an important issue in signal analysis and classification. Recently, time-frequency analysis has been investigated to detect the nonstationarity of a given signal, by constructiing from it a set of surrogate, stationarized signals. Time-frequency features are extracted to test the stationarity. Our paper is a further contribution by exploring the powerful framework of time-frequency learning machines. We show that one can relate stationarity to the structure of surrogates spectrograms and detect nonstationarity using a one-class classification approach. The proposed method does not suffer from any prior knowledge for extracting features, since it uses the entire time-frequency information. Using spherical multidimensional scaling technique, we illustrate the relevance of the proposed approach with simulation results.

Mots clés: One-class classification; Surrogates; Stationarity test; Kernel machines; Time-frequency analysis

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

Hassan Amoud, Paul Honeine, Cédric Richard, Pierre Borgnat, Patrick Flandrin. Time-Frequency Learning Machines For NonStationarity Detection Using Surrogates. SSP'09 (IEEE/SP 15th Workshop on Statistical Signal Processing 2009), IEEE/SP, Aug 2009, Cardiff, United Kingdom. pp.565-568, ⟨10.1109/SSP.2009.5278514⟩. ⟨ensl-00420575⟩