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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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Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Thursday, October 15, 2020 - 11:05:00 AM
Last modification on : Wednesday, January 26, 2022 - 11:05:21 AM


  • HAL Id : ensl-02967833, version 1


Pierre Borgnat, Patrick Flandrin, Cédric Richard, André Ferrari, Hassan Amoud, et al.. Time-Frequency Learning Machines For NonStationarity Detection Using Surrogates. Michael J. Way; Jeffrey D. Scargle; Kamal M. Ali; Ashok N. Srivstava. Advances in Machine Learning and Data Mining for Astronomy, Chapman & Hall; CRC Press, pp.487-503, 2012, 978‐1‐4398‐4173‐0. ⟨ensl-02967833⟩



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