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Decomposition of time-frequency distribution kernels
SPIE Proceedings, 1992Cohen'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 +1 more
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Divergence measures for time-frequency distributions
Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 2003Distance 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, 1991Recently, 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, 1989We 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, 1997A 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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ACOUSTIC CLASSIFICATION USING TIME-FREQUENCY DISTRIBUTIONS
Spring Conference Acoustics 2004, 2023H MARVI, I PARASKEVAS, E CHILTON
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