  High-resolution time-frequency (TF) analysis plays crucial role in
characterizing multicomponent signal (MCSs) and estimating oscillatory
properties. Linear time-frequency representations (TFRs) such as classical
short-time Fourier transform (STFT) and continuous wavelet transform (CWT)
incur constrained TF resolution and energy diffusion in both time and frequency
direction. The synchrosqueezing transform (SST) represents a powerful sparse
reassignment method that allows component reconstruction. This work introduces
SST as extension to STFT and CWT and illustrates corresponding advantages of
sharpening TFRs and recovery of instantaneous components. The SST effectiveness
is assessed in practical situations that involve comparing STFT-based and
CWT-based versions of synthetic data and also applying SST to optimize deep
learning (DL) prediction model. It is demonstrated how SST achieves promising
results in terms of improving TFR readability and increasing accuracy of
DL-based prediction models.
