In particular, I'm looking at signals containing amplitude and frequency modulated components.
One technique which particularly interests me is the Hilbert-Huang transform, and a quick Google search found this document, which for me was an excellent introduction. The authors give examples of the decomposition of seismic signals, in a simple, non-mathematical manner. R code examples here.
I'm wondering how the HHT compares to using a conventional filter bank. For instance the first intrinsic mode function calculated would seem to produce the lowest frequency component of the signal.
As the document gives coding examples in R, I intend to experiment with decomposition of musical (.WAV files), and will report back if I find anything of note.
In the meantime does anyone have experience of the Hilbert-Huang transform, and if so, would you care to share your experiences ?
very interesting, I'd like to test HHT with animal sounds and even if maybe not suitable, I would also very if it can be used to discriminate among animal vocalizations and other types of sounds and noises of not biological origin.
I don't find the link for the complementary materials (source code etc). Do you have it ?
Hi Tim & Gianni,
I have requested a link to the code from the authors.
The R code for Fig 2 (given in the document) seems to run ok, once line 5 is changed to ;
Also, you may find the hht R package of interest.
Agh! You had a link in there, I just didn't see it last time -- I forget that my phone doesn't color-code links well at all.
Interesting. I have a very intense pet peeve about the term "nonlinear signal" -- linearity is simply not a property of a signal, but rather of a system that may have generated that signal. So that put me off. But the rest of the paper (for analyzing signals that come from non-linear systems) is interesting.
May be good for separating a signal into chirps or other frequency-varying quasi-periodic components.
Here is the link to R code examples from the Hilbert-Huang Transform document.
My thanks to the author Danny Bowman for this :