Spectral decomposition of time-frequency distribution kernels.

It is shown that the singular-value decomposition (SVD) of the time-frequency (t-f) kernels allows the expression of the time-frequency distributions in terms of weighted sum of smoothed pseudo Winger-Ville distributions or modified periodograms, which are the two basic nonparametric power distributions for stationary and nonstationary signals, respectively. The windows appearing in the decomposition take zero and/or negative values and, therefore, are different than the time and lag windows commonly employed by these two distributions. The decomposition windows can be data-dependent or fixed, depending on whether the interest is to approximate the t-f distribution for a given data record, or for a Gaussian stationary white noise process [1].

Main Author: Amin, Moeness G.
Format: Villanova Faculty Authorship
Language: English
Published: 1992
Online Access: http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:173663
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dc_source_str_mv Proceedings of the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 1992.
author Amin, Moeness G.
author_facet_str_mv Amin, Moeness G.
author_or_contributor_facet_str_mv Amin, Moeness G.
author_s Amin, Moeness G.
spellingShingle Amin, Moeness G.
Spectral decomposition of time-frequency distribution kernels.
author-letter Amin, Moeness G.
author_sort_str Amin, Moeness G.
dc_title_str Spectral decomposition of time-frequency distribution kernels.
title Spectral decomposition of time-frequency distribution kernels.
title_short Spectral decomposition of time-frequency distribution kernels.
title_full Spectral decomposition of time-frequency distribution kernels.
title_fullStr Spectral decomposition of time-frequency distribution kernels.
title_full_unstemmed Spectral decomposition of time-frequency distribution kernels.
collection_title_sort_str spectral decomposition of time-frequency distribution kernels.
title_sort spectral decomposition of time-frequency distribution kernels.
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description It is shown that the singular-value decomposition (SVD) of the time-frequency (t-f) kernels allows the expression of the time-frequency distributions in terms of weighted sum of smoothed pseudo Winger-Ville distributions or modified periodograms, which are the two basic nonparametric power distributions for stationary and nonstationary signals, respectively. The windows appearing in the decomposition take zero and/or negative values and, therefore, are different than the time and lag windows commonly employed by these two distributions. The decomposition windows can be data-dependent or fixed, depending on whether the interest is to approximate the t-f distribution for a given data record, or for a Gaussian stationary white noise process [1].
publishDate 1992
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fgs.label Spectral decomposition of time-frequency distribution kernels.
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dc.title Spectral decomposition of time-frequency distribution kernels.
dc.creator Amin, Moeness G.
dc.description It is shown that the singular-value decomposition (SVD) of the time-frequency (t-f) kernels allows the expression of the time-frequency distributions in terms of weighted sum of smoothed pseudo Winger-Ville distributions or modified periodograms, which are the two basic nonparametric power distributions for stationary and nonstationary signals, respectively. The windows appearing in the decomposition take zero and/or negative values and, therefore, are different than the time and lag windows commonly employed by these two distributions. The decomposition windows can be data-dependent or fixed, depending on whether the interest is to approximate the t-f distribution for a given data record, or for a Gaussian stationary white noise process [1].
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dc.source Proceedings of the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 1992.
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