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Decomposition and statistical charac...

Aviyente, Sara Selin.

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Decomposition and statistical characterization of time-frequency distributions.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Decomposition and statistical characterization of time-frequency distributions./
作者:	Aviyente, Sara Selin.
面頁冊數:	155 p.
附註:	Source: Dissertation Abstracts International, Volume: 63-07, Section: B, page: 3398.
Contained By:	Dissertation Abstracts International63-07B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3057889
ISBN:	0493732810

Decomposition and statistical characterization of time-frequency distributions.
Aviyente, Sara Selin.

Decomposition and statistical characterization of time-frequency distributions. - 155 p.

Source: Dissertation Abstracts International, Volume: 63-07, Section: B, page: 3398.

Thesis (Ph.D.)--University of Michigan, 2002.

In the past twenty years, time-frequency distributions (TFDs) have become an indispensable tool for analyzing signals with time-varying spectra such as speech, music and biological signals. However, there are several shortcomings of the current methods that limit their usage, especially for signals in noise. In this dissertation, three different issues regarding the improvement of time-frequency distributions have been addressed: computational complexity, statistically unstable frequency marginals, and the lack of measures for quantitative interpretation of time-frequency representation's complexity.

ISBN: 0493732810Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Decomposition and statistical characterization of time-frequency distributions.
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245 1 0 $a Decomposition and statistical characterization of time-frequency distributions.
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500 $a Source: Dissertation Abstracts International, Volume: 63-07, Section: B, page: 3398.
500 $a Chair: William J. Williams.
502 $a Thesis (Ph.D.)--University of Michigan, 2002.
520 $a In the past twenty years, time-frequency distributions (TFDs) have become an indispensable tool for analyzing signals with time-varying spectra such as speech, music and biological signals. However, there are several shortcomings of the current methods that limit their usage, especially for signals in noise. In this dissertation, three different issues regarding the improvement of time-frequency distributions have been addressed: computational complexity, statistically unstable frequency marginals, and the lack of measures for quantitative interpretation of time-frequency representation's complexity.
520 $a The problem of computational complexity is addressed by extending the previous work in time-frequency kernel decomposition. Two new kernel decomposition methods that take advantage of the centrosymmetric structure and the scale invariance property of the kernels are introduced. The low computational complexity approximations to the time-frequency distributions are illustrated through examples.
520 $a The second problem that is discussed deals with the high variability of the frequency marginal for the current distributions. New time-frequency distributions yielding statistically stable frequency marginals, such as Thomson's multiwindow spectrum estimator, are derived and the performance of the new distributions is evaluated quantitatively.
520 $a Finally, Renyi entropy is adopted as the measure for quantifying the complexity of nonstationary signals on the time-frequency plane. Bounds on this quantity for random signals are derived and an approach for minimum entropy time-frequency kernel design is proposed. Entropy based signal processing algorithms such as detection, discrimination and denoising on the time-frequency plane are introduced.
590 $a School code: 0127.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Engineering, System Science. $3 1018128
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790 1 0 $a Williams, William J., $e advisor
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792 $a 2002
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