Time-frequency learning machines for nonstationarity detection using surrogates
Hassan Amoud, Paul Honeiné, Cédric Richard, Pierre Borgnat, Patrick Flandrin · 2009 IEEE/SP 15th Workshop on Statistical Signal Processing · 2009
An operational framework has recently been developed for testing stationarity of any signal relatively to an observation scale. The originality is to extract time-frequency features from a set of stationarized surrogate signals, and to use them for defining the null hypothesis of stationarity. Our paper is a further contribution that explores a general framework embedding techniques from machine learning and timefrequency analysis, called time-frequency learning machines. Based on one-class support vector machines, our approach uses entire time-frequency representations and does not require arbitrary feature extraction. Its relevance is illustrated by simulation results, and spherical multidimensional scaling techniques to map data to a visible 3D space.