Matlab

IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 50, NO. 12, DECEMBER 2002

2915

Spectrogram Segmentation by Means of Statistical
Features for Non-Stationary Signal Interpretation
Cyril Hory, Nadine Martin, and Alain Chehikian

Abstract—Time–frequency representations (TFRs) are suitable
tools for nonstationary signal analysis, but their reading is not
straightforward for a signalinterpretation 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 ofsegmentation. 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. Examplesare presented
concerning synthetic and real signals.
Index Terms— 2 distribution law, maximum likelihood, region
growing technique, statistical pattern recognition, time–frequency
analysis.

I. INTRODUCTION

T

HIS paper investigates a new method for the interpretation of nonstationary processes. This issue concerns the
problem of defining an automatic process to support a decisionfrom the analyzed signal. It is, for instance, the case of fault
detection in industrial control but also in many domains of application. The relevant information to be extracted from a nonstationary signal is included in the time evolution of its spectral content. Techniques based on time or frequency representations of the signal are not appropriate to provide such information. Several analysismethods called time–frequency representations (TFRs) have been proposed to represent a signal in
a hybrid space [7], [15]. A TFR displays the energy content of
a signal along both time and frequency dimensions. The components of the analyzed signal are described in this space by
structures called spectral patterns.
In the literature, many approaches have been proposed to
design automaticinterpretation technique involving TFR. Two
main classes can be drawn according to the position of the
time–frequency (TF) tool in the interpretation method. In the
first class, TFRs are fitted toward the objectives of the method.
This is the case, for example, of methods based on reassigned
TFRs [4], which provides an increased readability, or adaptive
Manuscript received August 2, 2001; revisedJuly 8, 2002. The associate editor coordinating the review of this paper and approving it for publication was
Dr. Chong-Yung Chi.
C. Hory and N. Martin are with the Laboratoire des Images et des Signaux (LIS), UMR 5083, CNRS-INP Grenoble, Grenoble, France (e-mail:
cyril.hory@lis.inpg.fr).
A. Chehikian is with the Université Joseph Fourier, Grenoble, France.
Digital Object Identifier10.1109/TSP.2002.805489

kernels of Cohen distributions [2], [5]. The method we propose
in this paper lies within the second class of method, where the
T-F interpretation is considered as a post-processing. In that
case, the interpretation task does not have any influence on the
performances of the T-F analysis as resolution or variance. It
is relevant if designed from the inner properties of theTFR.
We already proposed in [17] a processing of a TFR based on
mathematical morphology tools. This method was efficient for
straight spectral patterns segmentation but could not succeed
in detecting slowly varying structures because of the use of the
gradient function as shown in [14].
We present here a new method that is adapted to narrowband as
well as wideband components. We propose to...