Statistical hypothesis testing with time-frequency surrogates to check signal stationarity

Statistical hypothesis testing with time-frequency surrogates to check signal stationarity. Richard, C., Ferrari, A., Amoud, H., Honeine, P., Flandrin, P., & Borgnat, P. In Proc. 35th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3666-3669, Dallas, Texas, 14 - 19 March, 2010.

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Statistical hypothesis testing with time-frequency surrogates to check signal stationarity [pdf]

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An operational framework is developed for testing stationarity relatively to an observation scale. The proposed method makes use of a family of stationary surrogates for defining the null hypothesis of stationarity. As a further contribution to the field, we demonstrate the strict-sense stationarity of surrogate signals and we exploit this property to derive the asymptotic distributions of their spectrogram and power spectral density. A statistical hypothesis testing framework is then proposed to check signal stationarity. Finally, some results are shown on a typical model of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.

@INPROCEEDINGS{10.icassp.surrogates,
   author =  "Cédric Richard and André Ferrari and Hassan Amoud and Paul Honeine and Patrick Flandrin and Pierre Borgnat",
   title =  "Statistical hypothesis testing with time-frequency surrogates to check signal stationarity",
   booktitle =  "Proc. 35th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
   address =  "Dallas, Texas",
   month =  "14 - 19~" # mar,
   year  =  "2010",
   pages={3666-3669}, 
   doi={10.1109/ICASSP.2010.5495887}, 
   ISSN={1520-6149},
   acronym =  "ICASSP",
   url_link=  "https://ieeexplore.ieee.org/document/5495887",
   url_paper   =  "http://honeine.fr/paul/publi/10.icassp.surrogates.pdf",
   abstract={An operational framework is developed for testing stationarity relatively to an observation scale. The proposed method makes use of a family of stationary surrogates for defining the null hypothesis of stationarity. As a further contribution to the field, we demonstrate the strict-sense stationarity of surrogate signals and we exploit this property to derive the asymptotic distributions of their spectrogram and power spectral density. A statistical hypothesis testing framework is then proposed to check signal stationarity. Finally, some results are shown on a typical model of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.}, 
   keywords={probability, signal processing, time-frequency analysis, statistical hypothesis testing, time-frequency surrogates, signal stationarity, strict-sense stationarity, surrogate signals, spectrogram, power spectral density, Testing, Time frequency analysis, Spectrogram, Probability density function, Tellurium, Signal analysis, Signal processing, Feature extraction, Data mining, Machine learning, Time-frequency analysis, stationarity test, surrogate, spectrogram, probability density function}, 
}

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