Rights statement: Open Access funded by Engineering and Physical Sciences Research Council Under a Creative Commons license This is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing, 125, 2016 DOI: 10.1016/j.sigpro.2016.01.024
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Available under license: CC BY: Creative Commons Attribution 4.0 International License
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Extraction of instantaneous frequencies from ridges in time-frequency representations of signals
AU - Iatsenko, D.
AU - McClintock, Peter Vaughan Elsmere
AU - Stefanovska, Aneta
N1 - This is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing, 125, 2016 DOI: 10.1016/j.sigpro.2016.01.024 Open Access funded by Engineering and Physical Sciences Research Council Under a Creative Commons license
PY - 2016/8
Y1 - 2016/8
N2 - In signal processing applications, it is often necessary to extract oscillatory components and their properties from time-frequency representations, e.g. the windowed Fourier transform or wavelet transform. The first step in this procedure is to find an appropriate ridge curve: a sequence of amplitude peak positions (ridge points), corresponding to the component of interest and providing a measure of its instantaneous frequency. This is not a trivial issue, and the optimal method for extraction is still not settled or agreed. We discuss and develop procedures that can be used for this task and compare their performance on both simulated and real data. In particular, we propose a method which, in contrast to many other approaches, is highly adaptive so that it does not need any parameter adjustment for the signal to be analysed. Being based on dynamic path optimization and fixed point iteration, the method is very fast, and its superior accuracy is also demonstrated. In addition, we investigate the advantages and drawbacks that synchrosqueezing offers in relation to curve extraction. The codes used in this work are freely available for download.
AB - In signal processing applications, it is often necessary to extract oscillatory components and their properties from time-frequency representations, e.g. the windowed Fourier transform or wavelet transform. The first step in this procedure is to find an appropriate ridge curve: a sequence of amplitude peak positions (ridge points), corresponding to the component of interest and providing a measure of its instantaneous frequency. This is not a trivial issue, and the optimal method for extraction is still not settled or agreed. We discuss and develop procedures that can be used for this task and compare their performance on both simulated and real data. In particular, we propose a method which, in contrast to many other approaches, is highly adaptive so that it does not need any parameter adjustment for the signal to be analysed. Being based on dynamic path optimization and fixed point iteration, the method is very fast, and its superior accuracy is also demonstrated. In addition, we investigate the advantages and drawbacks that synchrosqueezing offers in relation to curve extraction. The codes used in this work are freely available for download.
KW - Ridge analysis
KW - Wavelet ridges
KW - Time-frequency representations
KW - Wavelet transform
KW - Windowed Fourier transform
KW - Instantaneous frequency
KW - Synchrosqueezing
U2 - 10.1016/j.sigpro.2016.01.024
DO - 10.1016/j.sigpro.2016.01.024
M3 - Journal article
VL - 125
SP - 290
EP - 303
JO - Signal Processing
JF - Signal Processing
SN - 0165-1684
ER -