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Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

Topics in time-frequency representations

Leon W. Cohen

Published Oct 22, 1996• 5 citations
Publisher

Abstract

We discuss some issues relevant to time-frequency analysis. Among them are, time-frequency spatial/spatial-frequency representations, generalized marginals and generalized characteristic functions. We derive representations that jointly involve the four variables, time, frequency, space, and spatial frequency. We apply these representations to wave packet motion. For the study on generalized marginals we show that there is an inherent difference between the standard methods of transforming variables and in time- frequency analysis. We use the method of generalized characteristic function operator method to generalize Taylor's theorem.

DOI
10.1117/12.255436

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We discuss some issues relevant to time-frequency analysis. Among them are, time-frequency spatial/spatial-frequency representations, generalized marginals and generalized characteristic functions. We derive representations that jointly involve the four variables, time, frequency, space, and spatial frequency. We apply these representations to wave packet motion. For the study on generalized marginals we show that there is an inherent difference between the standard methods of transforming variables and in time- frequency analysis. We use the method of generalized characteristic function operator method to generalize Taylor's theorem.
We present a general method for calculating relevant moments of the wavelet transform.Explicit results are given for the time, frequency, and scale moments.Using the results obtained, we show that the wavelet transform has unique characteristics that are not possessed by other methods.We discuss whether these unusual characteristics are physically or mathematically important.Exactly solvable examples are given, and the results are contrasted to those of the standard methods such as the spectrogram and the Wigner distribution.
We give a simple derivation for the covariance of time and frequency. The simplicity remains when applied to arbitrary variables. We show that the covariance enters into the uncertainty principle in a fundamental way. Also we present a seeming paradox: the uncertainty principle depends only on the marginals, but the marginals carry no information about covariance. Then how is it possible that the uncertainty principle involves the covariance? The resolution of the paradox clarifies a number of issues regarding the existence of well behaved manifestly positive joint distributions of time and frequency.>
In the previous chapters, we have thoroughly discussed the 2-D CWT and some of its applications. Then we have made the connection with the group theoretical origins of the method, thus establishing a general framework, based on the coherent state formalism. In the present chapter, we will apply the same technique to a number of different situations involving higher dimensions: wavelets in 3-D space ℝ 3 , wavelets in ℝ n ( n > 3), and wavelets on the 2-sphere S 2 . Then, in the next chapter, we will treat time-dependent wavelets, that is, wavelets on space–time, designed for motion analysis. In all cases, the technique is the same. First one identifies the manifold on which the signals are defined and the appropriate group of transformations acting on the latter. Next one chooses a square integrable representation of that group, possibly modulo some subgroup. Then one constructs wavelets as admissible vectors and derives the corresponding wavelet transform. Three-dimensional wavelets Some physical phenomena are intrinsically multiscale and three-dimensional. Typical examples may be found in fluid dynamics, for instance the appearance of coherent structures in turbulent flows, or the disentangling of a wave train in (mostly underwater) acoustics, as discussed above. In such cases, a 3-D wavelet analysis is clearly more adequate and likely to yield a deeper understanding [56].