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Testing stationarity with surrogates: A timefrequency approach
 IEEE TRANS. SIGNAL PROCESSING
, 2009
"... An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local timefrequency features. The originality is to make use of a family of stationary s ..."
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Cited by 11 (4 self)
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An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local timefrequency features. The originality is to make use of a family of stationary surrogates for defining the null hypothesis of stationarity and to base on them two different statistical tests. The first one makes use of suitably chosen distances between local and global spectra, whereas the second one is implemented as a oneclass classifier, the timefrequency features extracted from the surrogates being interpreted as a learning set for stationarity. The principle of the method and of its two variations is presented, and some results are shown on typical models of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.
TESTING STATIONARITY WITH TIMEFREQUENCY SURROGATES
, 2007
"... A method is proposed for testing stationarity in an operational sense, i.e., by both including explicitly an observation scale in the definition and elaborating a stationarized reference so as to reject the null hypothesis of stationarity with a controlled level of statistical significance. While th ..."
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Cited by 8 (4 self)
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A method is proposed for testing stationarity in an operational sense, i.e., by both including explicitly an observation scale in the definition and elaborating a stationarized reference so as to reject the null hypothesis of stationarity with a controlled level of statistical significance. While the approach is classically based on comparing local vs. global features in the timefrequency plane, the test operates with a family of stationarized surrogates whose analysis allows for a characterization of the null hypothesis. The general principle of the method is outlined, practical issues related to its actual implementation are discussed and a typical example is provided for illustrating the approach and supporting its effectiveness.
Testing stationarity with surrogates  A oneclass SVM approach
 IN PROC. IEEE STAT. SIG. PROC. WORKSHOP SSP07, MADISON (WI
"... An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local timefrequency features. The originality is to make use of a family of stationary su ..."
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Cited by 6 (5 self)
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An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local timefrequency features. The originality is to make use of a family of stationary surrogates for defining the null hypothesis and to base on them a statistical test implemented as a oneclass Support Vector Machine. The timefrequency features extracted from the surrogates are considered as a learning set and used to detect departure from stationnarity. The principle of the method is presented, and some results are shown on typical models of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.
SIGNALDEPENDENT TIMEFREQUENCY REPRESENTATIONS FOR CLASSIFICATION USING A RADIALLY GAUSSIAN KERNEL AND THE ALIGNMENT CRITERION
"... In this paper, we propose a method for tuning timefrequency distributions with radially Gaussian kernel within a classification framework. It is based on a criterion that has recently emerged from the machine learning literature: the kerneltarget alignement. Our optimization scheme is very similar ..."
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Cited by 3 (3 self)
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In this paper, we propose a method for tuning timefrequency distributions with radially Gaussian kernel within a classification framework. It is based on a criterion that has recently emerged from the machine learning literature: the kerneltarget alignement. Our optimization scheme is very similar to that proposed by Baraniuk and Jones for signaldependent timefrequency analysis. The relevance of this approach of improving timefrequency classification accuracy is illustrated through examples. 1.
TIMEFREQUENCY LEARNING MACHINES FOR NONSTATIONARITY DETECTION USING SURROGATES
"... An operational framework has recently been developed for testing stationarity of any signal relatively to an observation scale. The originality is to extract timefrequency features from a set of stationarized surrogate signals, and to use them for defining the null hypothesis of stationarity. Our p ..."
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Cited by 3 (3 self)
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An operational framework has recently been developed for testing stationarity of any signal relatively to an observation scale. The originality is to extract timefrequency 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 timefrequency learning machines. Based on oneclass support vector machines, our approach uses entire timefrequency 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. Index Terms — Timefrequency analysis, stationarity test, machine learning, oneclass classification, surrogates.
Testing Stationarity with Surrogates: A TimeFrequency Approach
, 2010
"... An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local timefrequency features. The originality is to make use of a family of stationary ..."
Abstract
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An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local timefrequency features. The originality is to make use of a family of stationary surrogates for defining the null hypothesis of stationarity and to base on them two different statistical tests. The first one makes use of suitably chosen distances between local and global spectra, whereas the second one is implemented as a oneclass classifier, the timefrequency features extracted from the surrogates being interpreted as a learning set for stationarity. The principle of the method and of its two variations is presented, and some results are shown on typical models of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.