[PD] measuring entropy of a signal?

[Charles Z Henry]

Hi Ronni

How do you mean to do it?

Shannon entropy is not an independent measurement--the information in a
observation is relative to the distribution of all it's possible values.

If I just take one sample and it's evenly distributed between -0.98 and 1
and it's quantized in 0.02 increments (to make the math easier), then the
information of any value observed is:
-0.01*log(0.01)

Then--if I had a signal that's N samples long, I have N times as much
information.  Or perhaps think of it as a rate of information.

But for real numbers and continuous distributions, this doesn't work.  The
information in a single observation diverges.  So, doing that with floating
point numbers is not practical.

You often see Shannon entropy describing digital signals.  If the signal
just switches between 0 and 1, we can generate a distribution of the data
and see what the probability is empirically.  The entropy of each new
sample is relative to the distribution.  Likewise, then if you know the
maximum rate of switching, you can figure out the maximum rate of
information in the signal.

Just a few thoughts...

Chuck



On Tue, Feb 26, 2013 at 6:09 AM, ronni montoya <ronni.montoya at gmail.com>wrote:

> Hi , i was wondering if anybody have implemented the shannon entropy
> function in pd?
>
> Do anybody have tried measuring entropy of a signal?
>
>
> cheeers
>
>
>
> R.
>
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