Results 251 to 260 of about 1,433,403 (294)

High-spectral-resolution time-frequency distribution kernels

SPIE Proceedings, 1992
A new class of time-frequency distribution (TFD) kernels is introduced. Members in this class satisfy the desirable TFD properties and simultaneously provide local autocorrelation functions (LAF) that are amenable to high-frequency resolution modeling techniques.
Amin, Moeness G., Williams, William J.
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Instantaneous frequency and time-frequency distributions

IEEE International Symposium on Circuits and Systems, 2003
A discussion of instantaneous frequency from the point of view of joint time-frequency distributions is presented. From this perspective, instantaneous frequency is the average frequency at a particular time. This view forces the consideration of the standard deviation of instantaneous frequency at a given time.
L. Cohen, C. Lee
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Minimum entropy time-frequency distributions

Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496), 2002
We present an approach to designing discrete time-frequency distributions that are extremely localized in the time-frequency plane. These distributions, which satisfy the marginals, are constructed recursively by transferring energy among the points in the time-frequency distribution (TFD) in a direction which decreases the entropy of the TFD.
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Time frequency distribution using neural networks

Proceedings of the IEEE Symposium on Emerging Technologies, 2005., 2005
Inthis paper wepresent amethod ofobtaining aTime Frequency Distribution (TFD) ofasignal whose frequenc) components varywithtinme. Themethod employs Neural Networks (NN)whicharetrained by usingthe spectrograms ofseveral training signals asinput and TFDs thatare highlyconcentrated alongthe instantaneous frequencies oftheindividual components present ...
I. Shafi, J. Ahmad, null Syed Ismail Shah, F.M. Kashife   +3 more
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Bilinear time-frequency distributions

1994
One of the main features of wavelet and Gabor theories is that they aim at decomposing signals into elementary ones localized to a certain extent in the time-frequency plane. One way of making this notion of localization more precise is to use time-frequency distributions.
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Cross Hilbert time-frequency distributions

SPIE Proceedings, 1998
It is mathematically convenient to consider both positive nd negative frequencies in signal representation. This idea is critically important to time-frequency analysis. Usually, however one is presented with real signals. It is also well known that the analytic signal is formed using the Hilbert transform.
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Wavelet windowed time-frequency distribution decompositions

SPIE Proceedings, 1997
This paper outlines means of combining and reconciling concepts associated with Cohen's class of distributions and with the wavelet transform. Both have their assets and their liabilities. Previous work has shown that one can decompose any time-frequency distribution (TFD) in Cohen's class into a weighted sum of spectrograms.
William J. Williams, Tzuhsien Sang, Jeffrey C. O Neill, Eugene J. Zalubas   +3 more
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On generalized-marginal time-frequency distributions

IEEE Transactions on Signal Processing, 1996
We introduce a family of time-frequency (TF) distributions with generalized marginals, i.e., beyond the time-domain and the frequency-domain marginals, in the sense that the projections of a TF distribution along one or more angles are equal to the magnitude squared of the fractional Fourier transforms of the signal.
Xiang-gen Xia, Y. Owechko, B.H. Soffer, R.M. Matic   +3 more
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Instantaneous frequency and time-frequency distributions

1995 International Conference on Acoustics, Speech, and Signal Processing, 2002
It is generally stated that the conditional mean frequency of a time-frequency distribution (TFD) should equal the instantaneous frequency of the signal. The commonly accepted definition of instantaneous frequency as the derivative of the phase of the analytic signal sometimes leads to curious results.
B. Tacer, P.J. Loughlin
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Local stationarity and time-frequency distributions

SPIE Proceedings, 2006
We use time-frequency distributions to define local stationarity of a random process. We argue that local stationarity is achieved when the Wigner spectrum is approximately factorable. We show that when that is the case the autocorrelation function is the one considered by Silverman in 1957. Other time-frequency representations are also considered.
GALLEANI, Lorenzo, COHEN L, SUTER B.
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