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Titolo:
Generalized transfer function estimation using evolutionary spectral deblurring
Autore:
Shah, SI; Chaparro, LF; El-Jaroudi, A;
Indirizzi:
Univ Pittsburgh, Dept Elect Engn, Pittsburgh, PA 15261 USA Univ Pittsburgh Pittsburgh PA USA 15261 ct Engn, Pittsburgh, PA 15261 USA
Titolo Testata:
IEEE TRANSACTIONS ON SIGNAL PROCESSING
fascicolo: 8, volume: 47, anno: 1999,
pagine: 2335 - 2339
SICI:
1053-587X(199908)47:8<2335:GTFEUE>2.0.ZU;2-S
Fonte:
ISI
Lingua:
ENG
Soggetto:
TIME-FREQUENCY-DISTRIBUTIONS; SIGNALS;
Keywords:
bilinear distributions; deblurring; deconvolution; evolutionary spectrum; generalized transfer function; time-frequency analysis; time-frequency representations;
Tipo documento:
Letter
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
--discip_EC--
Citazioni:
21
Recensione:
Indirizzi per estratti:
Indirizzo: Shah, SI Univ Pittsburgh, Dept Elect Engn, Pittsburgh, PA 15261 USA Univ Pittsburgh Pittsburgh PA USA 15261 Pittsburgh, PA 15261 USA
Citazione:
S.I. Shah et al., "Generalized transfer function estimation using evolutionary spectral deblurring", IEEE SIGNAL, 47(8), 1999, pp. 2335-2339

Abstract

We present a method for estimating the generalized transfer function (GTF)of a time-varying filter from a time-frequency representation (TFR) of itsoutput. This method uses the fact that many TFR's can be written as blurred versions of the GTF. The approach minimizes the error between the TFR found from the data and that found by blurring the GTF. The problem as such has many solutions. We, therefore, additionally constrain it to minimize the distance between the GTF-based spectrum and the autoterms of the Wigner distribution, suppressing the cross terms using an appropriate signal dependent mask function. To illustrate the performance of the proposed procedure, we apply it to the spectral representation of speech signals and to signal masking.

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Documento generato il 09/04/20 alle ore 09:11:32