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From: magoldfish on 9 Feb 2006 19:05
I am interested in applying the EMD (empirical mode decomposition)
algorithm to some speech signals. I note in some of the papers, e.g.,
"The empirical mode decomposition and the Hilbert spectrum for
nonlinear and non-stationary time series," that authors display the
skeleton Hilbert transform of the IMFs (intrinsic mode functions). Can
someone explain how I can obtain the skeleton Hilbert transform, esp.
in terms of Matlab functions?

Thanks!
Marcus

From: pisz_na.mirek on 10 Feb 2006 17:29
magoldfish(a)gmail.com wrote:
> I am interested in applying the EMD (empirical mode decomposition)
> algorithm to some speech signals. I note in some of the papers, e.g.,
> "The empirical mode decomposition and the Hilbert spectrum for
> nonlinear and non-stationary time series," that authors display the
> skeleton Hilbert transform of the IMFs (intrinsic mode functions). Can
> someone explain how I can obtain the skeleton Hilbert transform, esp.
> in terms of Matlab functions?

Calculate IA and IF for each IMF and plot IA(IF,t)^2 (energy)
From: Rick Lyons on 15 Feb 2006 20:17
On 9 Feb 2006 16:05:42 -0800, magoldfish(a)gmail.com wrote:

>I am interested in applying the EMD (empirical mode decomposition)
>algorithm to some speech signals.

(snipped)
>
>Marcus
>

Hi Marcus,

I've read a tiny bit about EMD, but not nearly
enough to understand it subtleties.

Marcus, do you think, based on your experience,
that EMD is something that the "average" DSP
engineer should study? In different words, do
you think the benefits from learning EMD outweigh
the trouble it takes to learn about this
process?

Thanks,
[-Rick-]


From: magoldfish on 16 Feb 2006 20:19
> Hi Marcus,
>
> I've read a tiny bit about EMD, but not nearly
> enough to understand it subtleties.
>
> Marcus, do you think, based on your experience,
> that EMD is something that the "average" DSP
> engineer should study? In different words, do
> you think the benefits from learning EMD outweigh
> the trouble it takes to learn about this
> process?
I think it depends on the types of signals you are interested in
studying, and whether the applications are commercial.

The intuition behind the EMD is pretty cool-- decompose a signal into a
sum of zero-mean AM-FM components-- and the algorithm is incredibly
easy to understand. Unlike Fourier or wavelet analysis, the bases
functions (imfs) are adaptive, and data-dependent. Huang and others
argue that this gives emd an advantage for non-stationary, nonlinear
signal analysis. They report some amazing results in their papers.
And you can always try it out with the free matlab toolbox from:

http://perso.ens-lyon.fr/patrick.flandrin/emd.html

However, as another poster points out, perhaps somewhat too negatively,
the theory greatly lags the success of applications. Also, the
algorithm is very slow compared to FFTs. Finally, Huang (NASA) have at
least one patent on it, so it may not be suitable for commercial
applications.

Marcus

From: Rick Lyons on 16 Feb 2006 21:59
On 16 Feb 2006 17:19:46 -0800, magoldfish(a)gmail.com wrote:

>> Hi Marcus,
>>
>> I've read a tiny bit about EMD, but not nearly
>> enough to understand it subtleties.
>>
>> Marcus, do you think, based on your experience,
>> that EMD is something that the "average" DSP
>> engineer should study? In different words, do
>> you think the benefits from learning EMD outweigh
>> the trouble it takes to learn about this
>> process?
>I think it depends on the types of signals you are interested in
>studying, and whether the applications are commercial.
>
>The intuition behind the EMD is pretty cool-- decompose a signal into a
>sum of zero-mean AM-FM components-- and the algorithm is incredibly
>easy to understand. Unlike Fourier or wavelet analysis, the bases
>functions (imfs) are adaptive, and data-dependent. Huang and others
>argue that this gives emd an advantage for non-stationary, nonlinear
>signal analysis. They report some amazing results in their papers.
>And you can always try it out with the free matlab toolbox from:
>
>http://perso.ens-lyon.fr/patrick.flandrin/emd.html
>
>However, as another poster points out, perhaps somewhat too negatively,
>the theory greatly lags the success of applications. Also, the
>algorithm is very slow compared to FFTs. Finally, Huang (NASA) have at
>least one patent on it, so it may not be suitable for commercial
>applications.
>
>Marcus

Hi Marcus,

Thanks to you
(and the mysterious pisz_na.mirek(a)dionizos.zind.ikem.pwr.wroc.pl)
for your thoughts and opinions regarding the EMD algorithm.

I know that NASA touted the EMD algorithm as
an important breakthrough in signal analysis,
but it's nice to receive the real-world, practical,
opinions from you guys.

Thanks again & Regards,
[-Rick-]


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