Multidimensional empirical mode decomposition: Difference between revisions

Revision as of 17:53, 6 February 2023

Revision as of 17:54, 6 February 2023

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In [[signal processing]], '''multidimensional empirical mode decomposition''' ('''multidimensional EMD''') is an extension of the [[one-dimensional]] (1-D) [[Hilbert–Huang transform|EMD]] algorithm to a signal encompassing multiple dimensions. The [[Hilbert–Huang transform|Hilbert–Huang empirical mode decomposition]] (EMD) process decomposes a signal into intrinsic mode functions combined with the [[Hilbert spectral analysis]], known as the [[Hilbert–Huang transform]] (HHT). The multidimensional EMD extends the 1-D [[Hilbert–Huang transform|EMD]] algorithm into multiple-dimensional signals. This decomposition can be applied to [[image processing]], [[audio signal processing]], and various other multidimensional signals.