Joint time-frequency analysis: methods and applications
Joint time-frequency analysis: methods and applications
Localization of the complex spectrum: the S transform
IEEE Transactions on Signal Processing
Comments on “The Inverse S-Transform in Filters With Time-Frequency Localization”
IEEE Transactions on Signal Processing
Authors' Reply to Comments on “The Inverse S-Transform in Filters With Time-Frequency Localization”
IEEE Transactions on Signal Processing
The S-Transform and Its Inverses: Side Effects of Discretizing and Filtering
IEEE Transactions on Signal Processing
The -Transform From a Wavelet Point of View
IEEE Transactions on Signal Processing - Part I
The inverse S-transform in filters with time-frequency localization
IEEE Transactions on Signal Processing
Discrete inverse S transform with least square error in time-frequency filters
IEEE Transactions on Signal Processing
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The S transform is a useful linear time-frequency distribution with a progressive resolution. Since it is linear. it filters efficiently in a time-frequency domain by multiplying a mask function. Several different inverse algorithms exist which may result in different filtering effects. The conventional inverse S transform (IST) proposed by Stockwell et al. is efficient but suffers from time leakage during filtering. The recent algorithm proposed by Schimmel and Gallart has better time localization during filtering but suffers from a reconstruction error and the frequency leakage during filtering. In this paper, two new IST algorithms are proposed that have better time-frequency localization in filtering than the previous two methods.