A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is the signal complexity, quantified as the number of components in the signal. This paper describes a method for the estimation of this number of components of a signal using the short-term Rényi entropy of its time-frequency distribution (TFD). We focus on the characteristics of TFDs that make them suitable for such a task. The performance of the proposed algorithm is studied with respect to the parameters of the S-method TFD, which combines the virtues of both the spectrogram and the Wigner-Ville distribution. Once the optimal parameters of the TFD have been determined, the applicability of the method in the analysis of signals in low SNRs and rea...
This paper describes a procedure for the time-frequency analysis of signals, based on TFDs and IF es...
In this paper, the problem of choosing a method for time-frequency signal analysis is discussed. It ...
International audienceOur purpose is to develop an unsupervised method of extraction and characteriz...
A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is the s...
The time-frequency Rényi entropy provides a measure of complexity of a nonstationary multicomponent ...
Identification of different specific signal components, produced by one or more sources, is a proble...
This paper presents a method for extraction of different signal components from multicomponent mixtu...
In the past twenty years, time-frequency distributions (TFDs) have become an indispensable tool for ...
The subject area of time-frequency analysis is concerned with creating meaningful representations of...
This paper explores three groups of time–frequency distributions: the Cohen’s, affine, and reassigne...
The importance of Time-Frequency Distributions (TFDs) has been widely recognized in many fields afte...
A time-frequency distribution provides many advantages in the analysis of multicomponent non-station...
The detection of events in a stochastic signal has been a subject of great interest. One of the olde...
International audienceIn this paper, an automatic adaptive method for identification and separation ...
The generalized entropies of Rényi inspire new measures for estimating signal information and comple...
This paper describes a procedure for the time-frequency analysis of signals, based on TFDs and IF es...
In this paper, the problem of choosing a method for time-frequency signal analysis is discussed. It ...
International audienceOur purpose is to develop an unsupervised method of extraction and characteriz...
A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is the s...
The time-frequency Rényi entropy provides a measure of complexity of a nonstationary multicomponent ...
Identification of different specific signal components, produced by one or more sources, is a proble...
This paper presents a method for extraction of different signal components from multicomponent mixtu...
In the past twenty years, time-frequency distributions (TFDs) have become an indispensable tool for ...
The subject area of time-frequency analysis is concerned with creating meaningful representations of...
This paper explores three groups of time–frequency distributions: the Cohen’s, affine, and reassigne...
The importance of Time-Frequency Distributions (TFDs) has been widely recognized in many fields afte...
A time-frequency distribution provides many advantages in the analysis of multicomponent non-station...
The detection of events in a stochastic signal has been a subject of great interest. One of the olde...
International audienceIn this paper, an automatic adaptive method for identification and separation ...
The generalized entropies of Rényi inspire new measures for estimating signal information and comple...
This paper describes a procedure for the time-frequency analysis of signals, based on TFDs and IF es...
In this paper, the problem of choosing a method for time-frequency signal analysis is discussed. It ...
International audienceOur purpose is to develop an unsupervised method of extraction and characteriz...