Stationarity index for abrupt changes detection in the time-frequency plane

This paper presents a new method based on time frequency representations (TFRs) for detecting abrupt changes in non-stationary noisy signals, Stationarity indices (SIs), based on different distance measures between TFRs, are defined and a comparison of the performances is performed using ROC curves and statistical properties. Two sets of signals are specially studied here: signals presenting very short segments and nonstrictly stepwise stationary signals. For these signals, classical methods based on autoregressive (AR) spectral estimators are often unsuccessful.

[1]  Ulrich Appel,et al.  Adaptive sequential segmentation of piecewise stationary time series , 1983, Inf. Sci..

[2]  Michèle Basseville,et al.  Sequential detection of abrupt changes in spectral characteristics of digital signals , 1983, IEEE Trans. Inf. Theory.

[3]  F. Hlawatsch,et al.  Linear and quadratic time-frequency signal representations , 1992, IEEE Signal Processing Magazine.

[4]  Analyse et reconnaissance des signaux a modulations numériques rapides à l'aide de transformations temps-fréquence , 1993 .

[5]  Richard Baraniuk,et al.  Time-frequency based distance and divergence measures , 1994, Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis.

[6]  Olivier J. J. Michel,et al.  Time-frequency complexity and information , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  I. Vincent,et al.  Non stationary signals classification using time-frequency distributions , 1994, Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis.

[8]  Isabelle Vincent.  Classification de signaux non-stationnaires , 1995 .

[9]  C. Doncarli,et al.  Abrupt changes detection in the time-frequency plane , 1996, Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96).