Results 261 to 270 of about 1,433,403 (294)

Fast implementation of time-frequency distributions

[1992] Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, 2003
Cohen's class of bilinear time-frequency energy distributions (TFDs) offer improved resolution over linear time-frequency representations (TFRs). Several problems with TFDs persist. Many TFDs are costly to evaluate, nonrepresentational, and not associated with low-cost signal synthesis algorithms.
G.S. Cunningham, W.J. Williams
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Positive Time-Frequency Distributions via Quadratic Programming

Multidimensional Systems and Signal Processing, 1998
As is known, the Fourier Transform approach does not provide a good solution for nonstationary stochastic signals. A possible approach to such cases is provided by the so-called time-frequency analysis. There exist many ways to achieve it. One of them consists of a bilinear time-frequency representation, which needs an explicit description of a kernel ...
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Spectrogram decompositions of time-frequency distributions

Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467), 2002
This paper outlines means of combining spectrograms formed using specially designed windows. Previous work has shown that one can decompose any time-frequency distribution (TFD) in Cohen's class into a weighted sum of spectrograms. This is accomplished by decomposing the kernel of the distribution in terms of an orthogonal set of analysis windows.
W.J. Williams, S. Aviyente
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Kernel decomposition of time-frequency distributions

IEEE Transactions on Signal Processing, 1994
Bilinear time-frequency distributions (TFDs) offer improved time-frequency resolution over linear representations, but suffer from difficult interpretation, higher implementation cost, and the lack of associated low-cost signal synthesis algorithms. In the paper, the authors introduce some new tools for the interpretation and quantitative comparison of
G.S. Cunningham, W.J. Williams
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Decomposition of time-frequency distribution kernels

SPIE Proceedings, 1992
Cohen's class of time-frequency distributions has been recognized to have significant potential for the analysis of complicated signals. The spectrogram, though it offers comparatively lower time-frequency resolution than other, more recently investigated members of Cohen's class, is still the most broadly used TFD today.
Gregory S. Cunningham, William J. Williams   +1 more
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Divergence measures for time-frequency distributions

Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 2003
Distance measures between statistical models or between a model and observations are widely used concepts in signal processing. They are commonly used in solving problems such as detection, automatic segmentation, classification, pattern recognition and coding. In recent years, there has been an interest in extending these distance measures to the time-
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Computing time-frequency distributions (signal analysis)

IEEE Transactions on Signal Processing, 1991
Recently, numerous strategies have been proposed for computing discrete time-frequency distributions such as the Wigner distribution. The author describes an efficient and straightforward strategy for computing time-frequency distributions that are members of Cohen's class. The strategy is based on the work by A.H.
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Wavelet Transform And Time-Frequency Distributions

SPIE Proceedings, 1989
We define the modulus squared of the wavelet transform to be the wavelet estimate, and express it in terms of any bilinear joint time-frequency distribution characterized by a product kernel.
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S-class of time–frequency distributions

IEE Proceedings - Vision, Image, and Signal Processing, 1997
A new general class of distributions (S-class of distributions) for time–frequency signal analysis is proposed. This class is derived by generalising recently defined S-distribution. It is possible to define the S-counterpart distribution for each known distribution from the Cohen class, such that some of the performances may be improved. This class of
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Positive Time–Frequency Distributions

2018
Patrick Loughlin, Leon Cohen
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