Whereas natural signals are nonlinear and nonstationary, their modeling and analysis have always been complicated tasks. The wavelet signal transform method features a more effective mechanism for the localization, analysis, approximation, and compression of signals in frequency and time, with higher resolution than other traditional ways. So the trend of wavelet-based analysis for natural signals has become more popular than ever, and plenty of investigations are carried out to increase the efficiency, performance, and precision of the related algorithms and implementation procedures. In this regard, the paper introduces the instantaneous frequency-embedded synchrosqueezing wavelet transform (IFE-SST) method and demonstrates its supremacy over the continuous wavelet transform (CWT), where both methods are extensions of the wavelet transform technique.

An introduction with a good literature review is the vanguard of the paper. An explanation of the synchrosqueezing transform (SST) follows, including discussions of CWT and generalized SST. A contrastive analysis debate about the bump wavelet and the Morlet wavelet features the topic of CWT, whereas a reference instantaneous frequency (IF) function formulation is the main approach in SST. With stepwise development, the lemmas and theorems of IFE-SST--the key topic and contribution of the paper--are theorized next. The paper then formalizes the instantaneous frequency-embedded CWT (IFE-CWT), and over it constructs the idea of IFE-SST and demonstrates the recovering mechanisms of the original signal. Based on the defined theorems, related algorithms for IFE-SST implementation, calculation of IFE-SST of monocomponent signals, and IFE-SST-based IF estimation of monocomponent signals are provided in a well-structured context. IFE-SST-based signal separation for multicomponent signals is the paper’s next noteworthy contribution.

The paper is a good resource; despite its merits, however, there is room for a comparative discussion of the various signaling techniques mentioned.