Journal
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
Volume 460, Issue 2044, Pages 955-975Publisher
ROYAL SOC
DOI: 10.1098/rspa.2003.1199
Keywords
denoising; empirical mode decomposition; Hilbert spectrum; instantaneous frequency; wavelet packets; wavelet transform
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Non-stationary signals are increasingly aualysed in the time-frequency domain to determine the variation of frequency components with time. It, was recently proposed in this journal that such signals could be analysed by projections onto the time-frequency plane giving, a set of monocomponent signals. These could then be converted to 'analytic' signals using the Hilbert transform and their instantaneous frequency calculated, which when weighted by the energy yields the 'Hilbert energy spectrum' for that projection. Agglomeration over projections yields the complete Hilbert spectrum. We show that superior results can be obtained using wavelet-based projections. The maximal-overlap (undecimated/stationary/translation-invariant) discrete wavelet transform and wavelet packet transforms are used, with the FejerKorovkin class of wavelet filters. These transforms produce decompositions which are conducive to statistical analysis, in particular enabling noise-reduction methodology to be developed and easily and successfully applied.
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