Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
A measure of some time-frequency distributions concentration
Signal Processing - Special section on digital signal processing for multimedia communications and services
SIAM Journal on Scientific Computing
Application of cyclostationary and time-frequency signal analysis to car engine diagnosis
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
Renyi information and signal-dependent optimal kernel design
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Time--frequency and time--time filtering with the S-transform and TT-transform
Digital Signal Processing
Hybrid FM-polynomial phase signal modeling: parameter estimationand Cramer-Rao bounds
IEEE Transactions on Signal Processing
Localization of the complex spectrum: the S transform
IEEE Transactions on Signal Processing
Selective Regional Correlation for Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Need for speed: fast Stockwell transform (FST) with O(N) complexity
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Expert Systems with Applications: An International Journal
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Energy concentration of the S-transform in the time-frequency domain has been addressed in this paper by optimizing the width of the window function used. A new scheme is developed and referred to as a window width optimized S-transform. Two optimization schemes have been proposed, one for a constant window width, the other for time-varying window width. The former is intended for signals with constant or slowly varying frequencies, while the latter can deal with signals with fast changing frequency components. The proposed scheme has been evaluated using a set of test signals. The results have indicated that the new scheme can provide much improved energy concentration in the time-frequency domain in comparison with the standard S-transform. It is also shown using the test signals that the proposed scheme can lead to higher energy concentration in comparison with other standard linear techniques, such as short-time Fourier transform and its adaptive forms. Finally, the method has been demonstrated on engine knock signal analysis to show its effectiveness.