Title :
Time-frequency learning machines for nonstationarity detection using surrogates
Author :
Amoud, Hassan ; Honeine, Paul ; Richard, Cédric ; Borgnat, Pierre ; Flandrin, Patrick
Author_Institution :
Inst. Charles Delaunay, Univ. de Technol. de Troyes, Troyes, France
Abstract :
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.
Keywords :
learning (artificial intelligence); signal detection; support vector machines; machine learning; nonstationarity detection; stationarized surrogate signals; support vector machines; time-frequency learning machines; timefrequency analysis; Feature extraction; Fourier transforms; Machine learning; Multidimensional systems; Signal analysis; Signal generators; Support vector machine classification; Support vector machines; Testing; Time frequency analysis; Time-frequency analysis; machine learning; one-class classification; stationarity test; surrogates;
Conference_Titel :
Statistical Signal Processing, 2009. SSP '09. IEEE/SP 15th Workshop on
Conference_Location :
Cardiff
Print_ISBN :
978-1-4244-2709-3
Electronic_ISBN :
978-1-4244-2711-6
DOI :
10.1109/SSP.2009.5278514