From: Jim Choate <ravage@einstein.ssz.com>

To: cypherpunks@toad.com

Message Hash: 7c7f4001fe7fca2a3f990aaafff342cb2b2dde66b41b8b398910a5390ef96880

Message ID: <199607250621.BAA07021@einstein.ssz.com>

Reply To: *N/A*

UTC Datetime: 1996-07-25 10:17:15 UTC

Raw Date: Thu, 25 Jul 1996 18:17:15 +0800

```
From: Jim Choate <ravage@einstein.ssz.com>
Date: Thu, 25 Jul 1996 18:17:15 +0800
To: cypherpunks@toad.com
Subject: Re: Digital Watermarks for copy protection in recent Billbo (fwd)
Message-ID: <199607250621.BAA07021@einstein.ssz.com>
MIME-Version: 1.0
Content-Type: text
Hi all,
The Sampling Theorem in operation is a little more complicated than the
model that is being discussed.
Forwarded message:
> From: mpd@netcom.com (Mike Duvos)
> Subject: Re: Digital Watermarks for copy protection in recent Billbo
> Date: Wed, 24 Jul 1996 21:38:36 -0700 (PDT)
> "Perry E. Metzger" <perry@piermont.com> writes:
>
> > The Nyquist Theorem states you need exactly twice the
> > samples, not over twice. The magic number isn't something
> > like 2.2, its exactly 2.
>
> The Sampling Theorem states that equally spaced instantaneous
> samples must be taken at a rate GREATER THAN twice the highest
> frequency present in the analog signal being sampled. If this is
> done, the samples contain all the information in the signal, and
> faithful reconstruction is possible.
Actualy the sampling theorem states that if you want to reproduce a signal
reliably it must be sampled at a frequency AT LEAST TWICE that of the
highest frequency of interest in the FFT of the signal. This means the
signal must be deformable into sine waves, not all signals qualify for this
particular limitation and in general are not good signals for sampling.
An example is a step change from v1 to v2. since there is no change in
voltage except for a brief moment the output of the FFT is pretty much flat.
What comes out the other end looks like a spike (similar to what happens when
you feed a square wave to a transformer and look at the output). If you
reconstruct this via the sampling theorem you get a sign wave of extremely
low amplitude and frequency.
If you think of a FFT as taking a signal and breaking it down into componant
frequencies. Then think of a hair comb where the lowest frequencies are the
bigger teeth (put them to the L. when looking at it). Because sign waves
have zero crossings a single sample per cycle is not enough. By taking at
least two samples a cycle you are guaranteed not to miss the presence or
absence of a componant frequency. So this guarantees that we get a accurate
count of componants and their phase relations to each other (where the
arbitrary time reference comes in - really a fixed frequency clock). Back to
the comb. Where a given signal has a componant leave the teeth. Where there
is no componant break them off. You are left with a ratty looking comb. Now
to each of the remaining teeth assign an amplitude for that specific
frequency that is consistent with the FFT you calculated. The way this FFT
is implimented in practice is called a 'Comb Filter' where it samples the
signal (wide bandwidth) over a set of very small bandwidth filters in
parallel. Scanning the output of the filters at a fixed rate you get a phase
relation as well. Digitize the signals in a particular pattern and you are
ready to cut your CD or whatever.
It does NOT guarantee faithful representation of the original signal but
rather a signal with the same energy spectrum and phase characteristics.
One of the basic ideas of Algebra is that any given curve can be explained
by a arbitrary set of equations. The Sampling Theorem just gives you a
rationale for picking from that set.
> Exactly twice the highest frequency won't do, and it should be
> obvious that sampling a sine wave at twice its frequency yields
> samples of constant magnitude and alternating sign which convey
> nothing about its phase and little useful about its amplitude
> either. (Drawing a little picture might be helpful here.)
Exactly what the theorem is supposed to produce. You take your original
signal and run it through a FFT. You look at the bandwidth you desire. The
highest frequency of interest is 1/2 or less your sampling frequency. With
this information you can build a set of sine waves whose amplitude is given
by the FFT along with phase relations. Since you are breaking the signal
into sine waves (which happen to be well defined) all you realy need is to
know the maximum and minimum amplitudes as well as their phase to some
arbitrary but constant time reference. Sum them back together on the other
side and what you got? A reasonable useable copy of your original signal.
This is why DSP's are optimized for multiplication (multiply the amplitude
of that componant by its presence in the FFT) and summing (add them together
to get the target signal). Generaly because of noise and similar phenomena
it is commen to multiply and add windows of samples (ie averaging).
> Although anything over twice the highest frequency will work in a
> theoretical sense, a small fudge factor does wonders for digital
> signal processing, if only to reduce to a reasonable value the
> width of the window into the sample stream needed for various
> signal manipulations.
Actualy I believe the decision was made by Philips (the inventor of the CD)
to settle on the 44kHz sample rate because of some design option it
simplified. I unfortunately don't remember anything more specific than that.
Anyone got the CD Rom bible?
Jim Choate
```

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- 1996-07-25 (Thu, 25 Jul 1996 18:17:15 +0800) - Re: Digital Watermarks for copy protection in recent Billbo (fwd) -
*Jim Choate <ravage@einstein.ssz.com>*- 1996-07-25 (Fri, 26 Jul 1996 03:06:03 +0800) - Re: Digital Watermarks for copy protection in recent Billbo (fwd) -
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