2015
DOI: 10.1016/j.sigpro.2014.08.010
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Synchrosqueezing-based time-frequency analysis of multivariate data

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Cited by 137 publications
(45 citation statements)
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“…The proposed technique for partitioning the ST coefficients along frequency uses a multivariate extension of a frequency tiling technique proposed in [28] based on multivariate bandwidth, so as to identify a set of modulated mul-3 See [26] for details on the implementation of the SST. tivariate oscillations [29] that are well separated in frequency.…”
Section: A Partitioning Of the Time-frequency Domainmentioning
confidence: 97%
See 1 more Smart Citation
“…The proposed technique for partitioning the ST coefficients along frequency uses a multivariate extension of a frequency tiling technique proposed in [28] based on multivariate bandwidth, so as to identify a set of modulated mul-3 See [26] for details on the implementation of the SST. tivariate oscillations [29] that are well separated in frequency.…”
Section: A Partitioning Of the Time-frequency Domainmentioning
confidence: 97%
“…The conventional hard and soft thresholding applied to the ST coefficients would yield discontinuities in the recovered signal of interest even in the absence of noise, which is not desirable. To this end, to capture the inter-channel dependencies that arise between multichannel signals we propose a thresholding technique that employs the multivariate instantaneous amplitude (29) Such thresholding is then directly applied to the multivariate instantaneous amplitude, as (30) where is the modified universal threshold (typical values for given in (5), is between 5 0.1-0.3). The recovered signal can then be obtained by summing the coefficients, , as follows (31) where corresponds to the denoised signal for each channel.…”
Section: B Denoising Using Synchrosqueezing Techniquesmentioning
confidence: 99%
“…The authors (Ahrabian et al, 2015) proposed a timefrequency algorithm using a synchrosqueezing transform and the concept of joint instantaneous frequency multivariate data. The raw signal will be involved in modulation.…”
Section: Signal Analysismentioning
confidence: 99%
“…This method reallocates the energies of resulting wavelet coefficients generated by the continuous wavelet transform (CWT) by combining the coefficients containing the same instantaneous frequency, re sulting in a highly localised time-frequency representation of a univariate signal. More recently a multivariate extension of the SST [19] has been proposed to identify a set of modulated oscillatory components common to the multivariate data. Ap plications include multivariate time-frequency analysis [19] and multivariate signal denoising [20], whereby inter-channel dependencies are employed for enhanced signal analysis.…”
Section: Introductionmentioning
confidence: 99%
“…More recently a multivariate extension of the SST [19] has been proposed to identify a set of modulated oscillatory components common to the multivariate data. Ap plications include multivariate time-frequency analysis [19] and multivariate signal denoising [20], whereby inter-channel dependencies are employed for enhanced signal analysis.…”
Section: Introductionmentioning
confidence: 99%