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Copulas: a New Insight Into Positive Time-Frequency Distributions Manuel Davy and Arnaud Doucet

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Copulas: a New Insight Into Positive Time-Frequency Distributions Manuel Davy and Arnaud Doucet IEEE SIGNAL PROCESSING LETTERS, VOL. 10, NO. 7, JULY 2003 215 Copulas: A New Insight Into Positive Time-Frequency Distributions Manuel Davy and Arnaud Doucet Abstract—In this letter, we establish connections between tively, ], and is a positive function such Cohen–Posch theory of positive time-frequency distributions that (TFDs) and copula theory. Both are aimed at designing joint prob- ability distributions with fixed marginals, and we demonstrate that they are formally equivalent. Moreover, we show that copula (3) theory leads to a noniterative method for constructing positive TFDs. Simulations show typical results. Index Terms—Copulas, imposed marginals, joint distribution, Positive TFDs are used in a variety of applications such as positive time-frequency. the analysis and synthesis of multicomponent signals [3], biomedical engineering [4], and speech processing [5]. More- I. INTRODUCTION over, efficient recursive implementations have been proposed OSITIVE Time-Frequency Distributions (TFDs) [1], [2] [6]–[10], most of which provide minimum cross-entropy P have the nice property of being positive and having cor- (MCE) TFDs, which requires the definition of a prior es- rect marginals. More precisely, let denote the time-domain timate such as the spectrogram. Aside the work of Cohen signal, and its positive TFD. Then we have and Posch [1], [2], statisticians [11], [12] have addressed the same problem, namely how to construct a joint probability for all distribution with imposed marginals? These researches have led to the concept of copula, and we show in this letter that the main result in copula theory, Sklar’s theorem, is equivalent to for all the formulation given in [2]. Each TFD admits a copula that contains all the information about the signal time-frequency dependence and that can be used to measure it. In addition to for all (1) providing a new insight into time-frequency theory, copulas provide a simple noniterative way to construct positive TFDs with correct marginals. These TFDs are proved to be different where is the time marginal, and from MCE TFDs and are easy to compute in practice. is the frequency marginal ( This letter is organized as follows. In Section II, we recall denotes the Fourier transform of some basic definitions and properties related to copulas. In ). For the sake of simplicity, we assume here that is Section III, we show that copulas and Cohen–Posch’s group of normalized such that . Whenever , positive TFDs are actually two versions of the same theory. A the results remain true up to the multiplicative factor . In [1] direct consequence of this connection is presented in Section IV and [2], it is shown that all distributions such as in (1) can be where we compute the copula of a Gaussian chirp, and we written as propose a new method aimed at computing positive TFDs for any signal. In Section V, we illustrate the interest of copulas in (2) time-frequency analysis by simulations. We give finally some conclusions and research directions in Section VI. where (respectively, ) denotes1 the cumulative distribution related to (respectively, )by [respec- II. COPULAS In this section, we consider cumulative distributions rather than noncumulative probability distributions. The elements pre- Manuscript received April 9, 2002; revised October 8, 2002. The work of M. Davy was supported by the European project MOUMIR. The associate editor sented here are taken from [13]. coordinating the review of this manuscript and approving it for publication was Definition 1: A copula is a function from to [0,1] Prof. See-May Phoon. such that M. Davy is with IRCCyN/CNRS, 44321 Nantes Cedex 03, France (e-mail: [email protected]). 1) for all ; A. Doucet is with the University of Cambridge, Department of Engineering, 2) and for all ; CB2 1PZ, UK (e-mail: [email protected]). 3) for all such that and Digital Object Identifier 10.1109/LSP.2003.811636 1Here, distributions are denoted with calligraphic letters (e.g., ), whereas cumulative distributions are denoted with boldface capital letters (e.g., ). (4) 1070-9908/03$17.00 © 2003 IEEE 216 IEEE SIGNAL PROCESSING LETTERS, VOL. 10, NO. 7, JULY 2003 Fig. 1. Three important copulas displayed as contour plots. Fig. 1 displays the contour plots of three important copulas: where is the copula related to . This mea- the product copula and the Fréchet–Hoeffding sure is maximum whenever is one of the bounds and , whose existence is justified by the following Fréchet–Hoeffding bounds. In this case, each of and is theorem: almost surely a strictly monotone function of the other [13, p. Theorem 2: Let be a copula. Let 170]. Another classical characterization of random variables and . Then, for all , dependence is mutual information . Let be such . that . Then, studying the We now expose the theorem that justifies the use of copulas dependence of using the mutual information in the study of joint distributions with imposed marginals. is equivalent to studying the copula entropy , since Theorem 3 (Sklar’s Theorem [11]): Let be a cumu- lative distribution with marginals and . Then there exists a copula such that (5) (10) If and are continuous, then is unique; otherwise where the copula entropy is defined by is uniquely defined on Range Range . Con- . versely, if and are distributions and is a copula, then defined as in (5) is a distribution function with III. REDEFINING POSITIVE TIME-FREQUENCY marginals and . DISTRIBUTIONS VIA COPULAS In practice, this theorem means that, whenever the marginals are fixed, a given probability distribution is uniquely related to In order to use copula theory in the context of positive TFDs, a copula and conversely. It also provides a way to construct the we notice that the cumulative distribution related to the copula from a given distribution (see Corollary 5). This requires, distribution of (2) is however, to define the quasi inverse of a marginal distribution (or equivalently, ). Definition 4: Let be a univariate distribution function. A quasi-inverse is such that (11) if Range (6) where otherwise (7) Corollary 5: Let be a cumulative distribution with (12) marginals and , and the corresponding copula. Then It is easy to verity that is actually a copula, and from Sklar’s (8) theorem, the copula defining a given positive TFD is unique. This result yields a new insight into Cohen–Posch’s positive These results are useful in practice. On the one hand, given TFDs, by providing a new way of constructing such TFDs: the two marginals and , one can construct a family of set of all possible positive TFDs with correct marginals is the distributions whose marginals are and . On the other set of functions such that hand, given a distribution , one can use its copula to characterize the dependence of the random (13) variables and . This can be done by using the following measure of dependence: where is any copula. All results from copula theory can then be applied to time-frequency analysis. For a given signal , however, most , or equivalently, most copulas (9) are useless, as they do not incorporate information about the ac- tual time-frequency energy location of . In order for DAVY AND DOUCET: COPULAS: NEW INSIGHT INTO POSITIVE TIME-FREQUENCY DISTRIBUTIONS 217 to represent effectively the time-frequency contents of ,itis Let be a “satisfactory” TFR of (i.e., a positive necessary to elaborate signal-dependent copulas. TFD that represents correctly the time-frequency contents of ) that however does not satisfy the marginal requirement. IV. TIME-FREQUENCY COPULAS Its cumulative marginals are denoted and . Then, con- In this section, we apply Sklar’s theorem and its corollary to struct the copula of (using Corollary 5) such that construct copulas for time-frequency distributions, called time- frequency copulas (TFCs). (21) A. TFC of a Simple Signal A still more satisfactory cumulative positive TFD (satisfying the marginals) of is then In order to obtain a better comprehension of copulas, we com- pute the TFC for an important class of signals. Consider a chirp (22) with Gaussian envelope [6] where and are the correct marginals. Typically, (14) can be the spectrogram of computed with the windowing function . Its cumulative marginal distributions are (15) C. Discussion This technique has many advantages. First, it is noniterative (16) as opposed to the optimization techniques in [6], [8], and [9]. Second, the information about the time-frequency dependence where . The Wigner–Ville distribu- is made free from “marginal effects”; in other words, the copula tion of [denoted ] is positive,2 and it has the cor- itself can be used to analyze the signal, measure its time-fre- rect marginals. is also the MCE TFD of [6] with quency dependence, or characterize a class of signals. Third, in the uniform distribution as prior. The cumulative distribution is the case of spectrogram-based TFCs, the choice of the window has little influence on the shape of the copula (provided the window length is reasonable), as will be shown in Section V. However, similar to [6] where the choice of the prior distribu- (17) tion had an influence on the resulting TFD, different choices of We can then compute the corresponding TFC in some special lead to slightly different positive TFDs. cases An important question is that of comparison with MCE TFDs. For a given spectrogram, the TFD computed with our method if (18) is visually very close to the MCE TFD obtained by choosing if (19) as prior the same spectrogram (see the simulations displayed if (20) in Section V). However, the closed-form calculations in Sec- tion IV-A show that the MCE TFD copula of a chirp (namely Equations (19) and (20) are obtained using or ) is not the same as the copula of the distribution chosen as , ,as tends prior (namely for the uniform prior), which shows that MCE to zero (where is the unit step), i.e., as the time support of TFDs are different from our TFDs.
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