Browsing by Author "Bayram, Metin"

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    Multiple Window Time Frequency Analysis
    (1996-06-01) Bayram, Metin; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We propose a robust method for estimating the time-varying spectrum of a non-stationary random process. Our approach extends Thomson's powerful multiple window spectrum estimation scheme to the time-frequency and time-scale planes. The method refines previous extensions of Thomson's method through optimally concentrated window and wavelet functions and a statistical test for extracting chirping line components.
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    Multiple Window Time Varying Spectrum Estimation
    (2000-01-20) Baraniuk, Richard G.; Bayram, Metin; Digital Signal Processing (http://dsp.rice.edu/)
    We overview a new non-parametric method for estimating the time-varying spectrum of a non-stationary random process. Our method extends Thomson's powerful multiple window spectrum estimation scheme to the time-frequency and time-scale planes. Unlike previous extensions of Thomson's method, we identify and utilize optimally concentrated Hermite window and Morse wavelet functions and develop a statistical test for extracting chirping line components. Examples on synthetic and real-world data illustrate the superior performance of the technique.
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    Multiple window time-frequency analysis
    (1996) Bayram, Metin; Baraniuk, Richard G.
    The bias-variance trade-off is an important issue is spectrum estimation. In 1982, Thomson introduced a powerful multiple window method for stationary signals that deals with the bias-variance trade-off in an optimal fashion. In this thesis, we extend Thomson's method to the time-frequency and time-scale planes, and propose a new method to estimate the time-varying spectrum of non-stationary random processes. Unlike previous extensions of Thomson's method, we identify and utilize optimally concentrated window and wavelet functions, and develop a statistical test for detecting chirping line components. The optimal windows are the Hermite functions for time-frequency analysis, and the Morse wavelets for time-scale analysis.