Extraction of pertinent subsets from time-frequency representations for detection and recognition purposes

Authors:
Benoit Leprettre;Nadine Martin
Affiliations:
INPG, Signals and Images Laboratory, BP 46, 38402 Saint Martin d'Hères, Cedex, France;INPG, Signals and Images Laboratory, BP 46, 38402 Saint Martin d'Hères, Cedex, France and Schneider Electric S.A., Corporate R&D, Algorithms & Signal Processing Dept., A2 Research Center, 4, ...
Venue:
Signal Processing
Year:
2002

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Abstract

A time-frequency representation can highlight non-stationarities in a signal We propose to extract subsets from the time-frequency representation (TFR) for classification or recognition purposes. We developed two approaches. The first one is developed for TFRs obtained from the short time Fourier transform or the gliding minimum variance method. The extraction of compact subsets is viewed as a segmentation of the TFR, which is performed by morphological filtering and watershed segmentation. The second approach is developed when the TFR has been obtained using parametric estimators. We consider a hybrid estimator, the ARCAP method, and use a Kalman filter trajectory tracker to extract spectral lines. The proposed methods are illustrated by examples on natural signals: dolphin whistle acoustical signals, cavitation signals and seismic signals produced by snow avalanches.