Time-Frequency Learning Machines For NonStationarity Detection Using Surrogates

Abstract : 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.
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SSP'09 (IEEE/SP 15th Workshop on Statistical Signal Processing 2009), Aug 2009, Cardiff, United Kingdom. IEEE, pp.565-568, 2009
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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), Aug 2009, Cardiff, United Kingdom. IEEE, pp.565-568, 2009. 〈ensl-00420575〉

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