Spectrogram segmentation by means of statistical features for non-stationary signal interpretation

Time-frequency representations (TFRs) are suitable tools for nonstationary signal analysis, but their reading is not straightforward for a signal interpretation task. This paper investigates the use of TFR statistical properties for classification or recognition purposes, focusing on a particular TFR: the spectrogram. From the properties of a stationary process periodogram, we derive the properties of a nonstationary process spectrogram. It leads to transform the TFR to a local statistical features space from which we propose a method of segmentation. We illustrate our matter with first- and second-order statistics and identify the information they, respectively, provide. The segmentation is operated by a region growing algorithm, which does not require any prior knowledge on the nonstationary signal. The result is an automatic extraction of informative subsets from the TFR, which is relevant for the signal understanding. Examples are presented concerning synthetic and real signals.