A new optimized Stockwell transform applied on synthetic and real non-stationary signals
The aim of this paper is to improve the energy concentration of the Stockwell transform (S-transform) in the timefrequency domain. A modified S-transform is proposed with several parameters to control the width of a hybrid Gaussian window. A constrained optimization problem is proposed based on an energy concentration measure as objective function and inequalities constraints to define the bounds of the Gaussian window. An active-set algorithm is applied to resolve the optimization problem. The optimization of the energy concentration in the timefrequency plane can lead to more reliable applications for non-stationary signals. The simulation results show a significant improvement of the proposed methodology most notably in the presence of noise comparing with the standard S-transform and existing modified S-transform in the literature. Moreover, comparison with other known timefrequency transforms such as Short-time Fourier transform (STFT) and smoothed-pseudo WignerVille distribution (SPWVD) is also performed and discussed. The proposed S-transform is tested also on real non-stationary signals through an example of split detection in heart sounds.
stockwell transform energy concentration timefrequency active-set algorithm heart sounds