Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Multitaper marginal time-frequency distributions
Signal Processing
IEEE Transactions on Signal Processing
Optimization of weighting factors for multiple window spectrogram of event-related potentials
EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
Time-frequency-based detection using discrete-time discrete-frequency Wigner distributions
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Optimized weighted averaging of peak matched multiple windowspectrum estimators
IEEE Transactions on Signal Processing
Optimal kernels for nonstationary spectral estimation
IEEE Transactions on Signal Processing
Discrete-time, discrete-frequency, time-frequency analysis
IEEE Transactions on Signal Processing
An overview of aliasing errors in discrete-time formulations oftime-frequency representations
IEEE Transactions on Signal Processing
Multiple window time-varying spectral analysis
IEEE Transactions on Signal Processing
A centrosymmetric kernel decomposition for time-frequency distribution computation
IEEE Transactions on Signal Processing
Toeplitz and Hankel kernels for estimating time-varying spectra ofdiscrete-time random processes
IEEE Transactions on Signal Processing
A multiple window method for estimation of peaked spectra
IEEE Transactions on Signal Processing
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This paper investigates the time-discrete multitapers that give a mean square error optimal Wigner spectrum estimate for a class of locally stationary processes (LSPs). The accuracy in the estimation of the time-variableWigner spectrum of the LSP is evaluated and compared with other frequently used methods. The optimal multitapers are also approximated by Hermite functions, which is computationally more efficient, and the errors introduced by this approximation are studied. Additionally, the number of windows included in a multitaper spectrum estimate is often crucial and an investigation of the error caused by limiting this number is made. Finally, the same optimal set of weights can be stored and utilized for different window lengths. As a result, the optimal multitapers are shown to be well approximated by Hermite functions, and a limited number of windows can be used for a mean square error optimal spectrogram estimate.