Precisions on Hilbert Huang transform
After going through the literature regarding HHT and EMD, I found that the "Huang" part of HHT comes from the fact that he is the one who proposed EMD in the first place. That explains the name of the method...

For more developments regarding EMD and HHT, I recommend the papers by Rilling et al. "On empirical mode decomposition and its algorithms". For the lucky ones who speak French, Rilling's PhD. thesis on EMD no longer seems to be available online; the document seems very complete and contains a very detailed mathematical analysis of EMD. Related articles are also available in English here on Google Scholar.

One may sum-up HHT this way:


EMD: decomposes the initial signal as a list of Intrinsic Mode Functions (IMF);
Hilbert tranform computes the instantaneous frequencies associated to the IMF (which are precisely well suited for such transformation)
Hilbert spectrum, meaning a representation of the amplitude of the IMF in a frequency/time domain using the instantaneous frequencies.


Here is a simple example based on the time signal: $y(t)=t^2sin(t^2)$ for $t\in[0;~16]$s. This signal features a quadratic growth of its amplitude combined with a linear growth of its frequency. Here is what a HHT of the signal leads to:

signal of interest



IMF obtained from EMD

$y(t)$">



instantaneous frequencies



Hilbert spectrum (white to black from 0 to maximum amplitude)



Some peaople see the HHT as a generalized Fourier Transform in the sense that the decomposition of the signal interest by HHT leads to both amplitude and frequency varying time signals.

A signifiant drawback of HHT lies in its sensitivity to edge effects (what happens for the signal close to its left and right boundaries in 1D). Several techniques exist in order to mitigate these effects. Articles by Rilling mentioned above go with mirroring strategies while other engineering oriented strategies may involve waveform matching techniques.