Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Evolutionary spectral analysis using a warped Gabor expansion
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
Data-adaptive evolutionary spectral estimation
IEEE Transactions on Signal Processing
Optimal kernels for nonstationary spectral estimation
IEEE Transactions on Signal Processing
Minimum-variance time-frequency distribution kernels
IEEE Transactions on Signal Processing
Multiple window time-varying spectral analysis
IEEE Transactions on Signal Processing
A theory of polyspectra for nonstationary stochastic processes
IEEE Transactions on Signal Processing
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in theory and methods for nonstationary signal analysis
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Time-frequency distributions (TFDs) belonging to Cohen's class yield a frequency marginal that is equivalent to the periodogram of the signal. It is well-known that the periodogram is not a good spectral estimator since it is not a consistent estimate, i.e. its variance does not decrease with the sample size. Thomson addressed this issue by introducing a multitaper spectrum estimator with high resolution and statistical stability [D.J. Thomson, Spectrum estimation and harmonic analysis, Proc. IEEE 70 (9) (1982) 1055-1096]. In recent years, various approaches have been developed to extend such multitaper spectral estimators to the area of nonstationary signal analysis [F.Çakrak, P. Loughlin, Multiple window nonlinear time-varying spectral analysis, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, 1998, pp. 2409-2412; F. Çakrak, P. Loughlin, Multiple window nonlinear time-varying spectral analysis, IEEE Trans. Signal Process. 49 (2) (2001) 448-453; J.W. Pitton, Time-frequency spectrum estimation: an adaptive multitaper method, in: Proceedings of IEEE International Symposium on Time-frequendcy and Time-scale analysis, 1998, pp. 665-668; J.W. Pitton, Nonstationary spectrum estimation and time-frequency concentration, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, 1998, pp. 2425-2428; G. Frazer, B. Boashash, Multiple window spectrogram and time-frequency distributions, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, 1994, pp. 293-296; M. Bayram, R.G. Baraniuk, Multiple window time-frequency analysis, in: Proceedings of the IEEE International Symposium on Time-frequency and Time-scale analysis, 1996, pp. 173-176.]. In this paper, a new method that approaches the problem from the perspective of frequency marginals is introduced. A class of time-frequency distributions, multitaper marginal TFD (MTM-TFD), is constructed for analyzing time-varying signals in noise with statistically stable frequency marginals. A kernel design method yielding any desired frequency marginal, such as provided by Thomson's spectrum estimator, is derived for a given signal. The improvement in the performance of this new class of time-frequency distributions compared to the conventional time-frequency distributions is illustrated through examples.