Open AccessTime‐frequency synchroextracting transform
Author(s) -
Zhang Ran,
Liu Xingxing,
Zheng Yongjun,
Lv Haotun,
Li Baosheng,
Yang Shenghui,
Tan Yu
Publication year - 2022
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/sil2.12073
Subject(s) - time–frequency analysis , s transform , robustness (evolution) , computer science , spectrogram , short time fourier transform , algorithm , fourier transform , energy (signal processing) , range (aeronautics) , fractional fourier transform , harmonic wavelet transform , constant q transform , hilbert–huang transform , speech recognition , artificial intelligence , mathematics , wavelet transform , computer vision , fourier analysis , statistics , discrete wavelet transform , filter (signal processing) , engineering , aerospace engineering , biochemistry , wavelet , gene , mathematical analysis , chemistry
The study proposes a novel time‐frequency analysis technique, titled time‐frequency synchroextracting transform which enhances the accuracy of synchroextracting transform. The synchroextracting transform is a powerful postprocessing method that sharpens the energy distribution of spectrogram. However, its limitation is the sole use of time or frequency factors that cannot accurately portray signals containing both ‘slow‐varying’ and ‘fast‐changing’ components. The proposed method applies the synchroextracting procedure in both time and frequency dimensions to improve the energy concentration. A fast Fourier transform‐‐based algorithm is also provided for efficient implementation. The proposed method is validated via numerical and empirical data. Compared with advanced methods including synchrosqueezing transform and synchroextracting transform, the proposed method is suitable to analyse wider range of signals and has better noise robustness than the original synchroextracting transform.