How was it known that mics were good before the advent of hi-fi playback? [Archive]

Don Pearce[_3_]

December 22nd 10, 11:35 AM

On Wed, 22 Dec 2010 03:15:36 -0800, "William Sommerwerck"
> wrote:

>"Don Pearce" > wrote in message
...
>> On Tue, 21 Dec 2010 18:37:54 -0800 (PST), muzician21
>> > wrote:
>
>>> I've read that certain vintage mics say from the 40's are considered
>>> desirable for their sound quality. So how did engineers of the day
>>> gauge the performance of mics if there wasn't a truly high quality
>>> playback system available?
>>> Or is that not correct?
>
>> A good place to start is right here
>> http://www.bbc.co.uk/rd/pubs/archive/
>> Numbers 4, 7 and 8 are good.
>
>These articles date from 1955 or later, not the '40s. Reasonably good
>speakers were available at that time, and the introduction of the ESL-57 in
>1957 greatly improved the ability to make rational decisions about
>microphone "quality".
>
>Thanks for the reference. There is a lot of interesting articles here...
>

There is a big chicken and egg question here, though. To assess a
microphone you need a better speaker. To assess a speaker you need a
better microphone. The BBC R&D department managed to find their way
into some first-principles methods to design and assess their
microphones without benefit of suitable speakers. That speaks of great
theoretical capability as well as superb engineering.

d

Mike Rivers

December 22nd 10, 01:00 PM

On 12/21/2010 9:37 PM, muzician21 wrote:
> I've read that certain vintage mics say from the 40's are considered
> desirable for their sound quality. So how did engineers of the day
> gauge the performance of mics if there wasn't a truly high quality
> playback system available?

Playback systems weren't all that bad. Also, and probably
more important, they didn't gauge the performance of the
mics. They used what was available. There really weren't
that many choices. They knew enough not to record the
symphony or jazz band using a communications mic, and there
were pretty much established studio standard mics that
everyone used. And most were as good as the best mics we
have today, when used in the same way.

--
"Today's production equipment is IT based and cannot be
operated without a passing knowledge of computing, although
it seems that it can be operated without a passing knowledge
of audio." - John Watkinson

http://mikeriversaudio.wordpress.com - useful and
interesting audio stuff

Kulin Remailer

December 22nd 10, 01:06 PM

>I've read that certain vintage mics say from the 40's are considered
>desirable for their sound quality.

A lot of old stuff sounded warm, especially because of those old crystal
mics and the tube electronics. Recorded music doesn't have to sound
identical to the original to sound good. A still life painting can still be
beautiful even though it's far from a photograph. Anyway since the PA
equipment was not like today's reference speakers and electronic amps the
reproductions often did sound almost identical to the live performance. I
can't speak to what happened in the 40's but I do remember to some degree
what music sounded like in the 50's ;-)

> So how did engineers of the day gauge the performance of mics if there
>wasn't a truly high quality playback system available?

They gauged it by using the best they could get that sounded the best on
the equipment they had at the time. You often don't miss what you never had
and everything is relative when it comes to sound and perception.

Arny Krueger

December 22nd 10, 01:13 PM

"Don Pearce" > wrote in message

> There is a big chicken and egg question here, though. To
> assess a microphone you need a better speaker.

Other sonic sources such as spark gaps exist.

> To assess
> a speaker you need a better microphone. The BBC R&D
> department managed to find their way into some
> first-principles methods to design and assess their
> microphones without benefit of suitable speakers. That
> speaks of great theoretical capability as well as superb
> engineering.

Speakers used for calibrating microphones need only produce a usable
response on axis, and at relatively low levels.

Microphone calibrators are essentially special purpose speakers.

In the US, RCA and the AT&T labs were leaders in the use of first-principles
means for evaluating and calibrating audio equipment including microphones.

William Sommerwerck

December 22nd 10, 02:27 PM

> There is a big chicken and egg question here, though. To assess
> a microphone you need a better speaker. To assess a speaker
> you need a better microphone. The BBC R&D department managed
> to find their way into some first-principles methods to design and
> assess their microphones without benefit of suitable speakers.
> That speaks of great theoretical capability as well as superb
> engineering.

There's also the fact that ribbon and "electrostatic" mics are inherently
superior to dynamic mics, for well-understood theoretical reasons, and the
designers were aware of this.

Scott Dorsey

December 22nd 10, 02:28 PM

muzician21 > wrote:
>I've read that certain vintage mics say from the 40's are considered
>desirable for their sound quality. So how did engineers of the day
>gauge the performance of mics if there wasn't a truly high quality
>playback system available?

They used the best playback system available. Which is all anyone can ever do.
--scott

--
"C'est un Nagra. C'est suisse, et tres, tres precis."

Don Pearce[_3_]

December 22nd 10, 02:57 PM

On Wed, 22 Dec 2010 08:13:09 -0500, "Arny Krueger" >
wrote:

>"Don Pearce" > wrote in message

>
>> There is a big chicken and egg question here, though. To
>> assess a microphone you need a better speaker.
>
>Other sonic sources such as spark gaps exist.
>

I've done spark gap tests of mics myself, and posted the results here.
Their problem is that they are only good for the top end of the
response range.

>> To assess
>> a speaker you need a better microphone. The BBC R&D
>> department managed to find their way into some
>> first-principles methods to design and assess their
>> microphones without benefit of suitable speakers. That
>> speaks of great theoretical capability as well as superb
>> engineering.
>
>Speakers used for calibrating microphones need only produce a usable
>response on axis, and at relatively low levels.
>
>Microphone calibrators are essentially special purpose speakers.
>

Yes, but the chicken and egg question is not addressed by any of
these.

>In the US, RCA and the AT&T labs were leaders in the use of first-principles
>means for evaluating and calibrating audio equipment including microphones.
>

At least with a capacitor microphone there is a purely electrical
method, which involves deflecting the diaphragm with an externally
applied voltage field, then letting go. The resulting step response
can be FFT'd into a frequency response that will be highly accurate.
Of course this is an integrated response - there are still the polar
irregularities to consider, but they too can be analysed
geometrically.

d

Ron Capik[_3_]

December 22nd 10, 03:40 PM

On 12/22/2010 9:57 AM, Don Pearce wrote:
> On Wed, 22 Dec 2010 08:13:09 -0500, "Arny >
> wrote:
>
>> "Don > wrote in message
>>
>>
>>> There is a big chicken and egg question here, though. To
>>> assess a microphone you need a better speaker.
>>
>> Other sonic sources such as spark gaps exist.
>>
>
> I've done spark gap tests of mics myself, and posted the results here.
> Their problem is that they are only good for the top end of the
> response range.
>
>>> To assess
>>> a speaker you need a better microphone. The BBC R&D
>>> department managed to find their way into some
>>> first-principles methods to design and assess their
>>> microphones without benefit of suitable speakers. That
>>> speaks of great theoretical capability as well as superb
>>> engineering.
>>
>> Speakers used for calibrating microphones need only produce a usable
>> response on axis, and at relatively low levels.
>>
>> Microphone calibrators are essentially special purpose speakers.
>>
>
> Yes, but the chicken and egg question is not addressed by any of
> these.
>
>> In the US, RCA and the AT&T labs were leaders in the use of first-principles
>> means for evaluating and calibrating audio equipment including microphones.
>>
>
> At least with a capacitor microphone there is a purely electrical
> method, which involves deflecting the diaphragm with an externally
> applied voltage field, then letting go. The resulting step response
> can be FFT'd into a frequency response that will be highly accurate.
> Of course this is an integrated response - there are still the polar
> irregularities to consider, but they too can be analysed
> geometrically.
>
> d
FFTs, in the 40's? I think not.

Later...
Ron Capik
--

Don Pearce[_3_]

December 22nd 10, 03:49 PM

On Wed, 22 Dec 2010 10:40:47 -0500, Ron Capik >
wrote:

>On 12/22/2010 9:57 AM, Don Pearce wrote:
>> On Wed, 22 Dec 2010 08:13:09 -0500, "Arny >
>> wrote:
>>
>>> "Don > wrote in message
>>>
>>>
>>>> There is a big chicken and egg question here, though. To
>>>> assess a microphone you need a better speaker.
>>>
>>> Other sonic sources such as spark gaps exist.
>>>
>>
>> I've done spark gap tests of mics myself, and posted the results here.
>> Their problem is that they are only good for the top end of the
>> response range.
>>
>>>> To assess
>>>> a speaker you need a better microphone. The BBC R&D
>>>> department managed to find their way into some
>>>> first-principles methods to design and assess their
>>>> microphones without benefit of suitable speakers. That
>>>> speaks of great theoretical capability as well as superb
>>>> engineering.
>>>
>>> Speakers used for calibrating microphones need only produce a usable
>>> response on axis, and at relatively low levels.
>>>
>>> Microphone calibrators are essentially special purpose speakers.
>>>
>>
>> Yes, but the chicken and egg question is not addressed by any of
>> these.
>>
>>> In the US, RCA and the AT&T labs were leaders in the use of first-principles
>>> means for evaluating and calibrating audio equipment including microphones.
>>>
>>
>> At least with a capacitor microphone there is a purely electrical
>> method, which involves deflecting the diaphragm with an externally
>> applied voltage field, then letting go. The resulting step response
>> can be FFT'd into a frequency response that will be highly accurate.
>> Of course this is an integrated response - there are still the polar
>> irregularities to consider, but they too can be analysed
>> geometrically.
>>
>> d
>FFTs, in the 40's? I think not.
>
Have you never worked an FFT by hand?

d

Scott Dorsey

December 22nd 10, 04:12 PM

Ron Capik > wrote:
>On 12/22/2010 9:57 AM, Don Pearce wrote:
>>
>> At least with a capacitor microphone there is a purely electrical
>> method, which involves deflecting the diaphragm with an externally
>> applied voltage field, then letting go. The resulting step response
>> can be FFT'd into a frequency response that will be highly accurate.
>> Of course this is an integrated response - there are still the polar
>> irregularities to consider, but they too can be analysed
>> geometrically.
>
>FFTs, in the 40's? I think not.

Gen-Rad swept sine filters tuned mechanically by a bicycle chain driven
off the chart recorder motor are as close as it got. Actually deriving
frequency response from impulse responses didn't happen until the eighties,
really.

THIS meant that in order to measure a microphone or speaker, you needed
either an accurate reference microphone OR an accurate reference sound source.

Building an accurate reference sound source using a mechanical siren or
pistonphone is possible, and Helmholtz did it long before the forties.
It has some severe limitations, the waveform is poor, and it usually has to
be used coupled into a tube and then free-field fudge factors applied, but
it works.

In the 1930s, though, Wente at Bell Labs came up with the Western Electric 640
reference microphone, which was flat and accurate on-axis in the free field
and whose abberations could be modelled mathematically in order to provide
fudge factors for use in other situations.

All of the Type I measurement microphones we use today are all derivations of
the 640AA design.

The 640AA was pretty noisy by modern standards but it was shockingly accurate.
Lower cost condenser mikes in the forties, like the Altec Cokebottle, were
basically derived from that design as well and were pretty decent performers.
Again, it's a lot easier to make an accurate omni than it is to make an
accurate cardioid, and it's a lot easier to make an accurate omni when you
don't care about noise.

So, really until the mid-thirties, making accurate measurements was hard,
but after the mid-thirties it wasn't so bad.
--scott

--
"C'est un Nagra. C'est suisse, et tres, tres precis."

William Sommerwerck

December 22nd 10, 04:42 PM

> At least with a capacitor microphone there is a purely electrical
> method, which involves deflecting the diaphragm with an externally
> applied voltage field, then letting go. The resulting step response
> can be FFT'd into a frequency response that will be highly accurate.
> Of course this is an integrated response -- there are still the polar
> irregularities to consider, but they too can be analysed
> geometrically.

The modern FFT did not exist until ca. 1967. I remember the IEEE article
about it. It was A Big Thing.

I don't see why the same sort of analysis could not be applied to a ribbon
or dynamic mic.

Ron Capik[_3_]

December 22nd 10, 04:52 PM

On 12/22/2010 10:49 AM, Don Pearce wrote:
> On Wed, 22 Dec 2010 10:40:47 -0500, Ron >
> wrote:
>
>> On 12/22/2010 9:57 AM, Don Pearce wrote:
>>> On Wed, 22 Dec 2010 08:13:09 -0500, "Arny >
>>> wrote:
>>>
>>>> "Don > wrote in message
>>>>
>>>>
>>>>> There is a big chicken and egg question here, though. To
>>>>> assess a microphone you need a better speaker.
>>>>
>>>> Other sonic sources such as spark gaps exist.
>>>>
>>>
>>> I've done spark gap tests of mics myself, and posted the results here.
>>> Their problem is that they are only good for the top end of the
>>> response range.
>>>
>>>>> To assess
>>>>> a speaker you need a better microphone. The BBC R&D
>>>>> department managed to find their way into some
>>>>> first-principles methods to design and assess their
>>>>> microphones without benefit of suitable speakers. That
>>>>> speaks of great theoretical capability as well as superb
>>>>> engineering.
>>>>
>>>> Speakers used for calibrating microphones need only produce a usable
>>>> response on axis, and at relatively low levels.
>>>>
>>>> Microphone calibrators are essentially special purpose speakers.
>>>>
>>>
>>> Yes, but the chicken and egg question is not addressed by any of
>>> these.
>>>
>>>> In the US, RCA and the AT&T labs were leaders in the use of first-principles
>>>> means for evaluating and calibrating audio equipment including microphones.
>>>>
>>>
>>> At least with a capacitor microphone there is a purely electrical
>>> method, which involves deflecting the diaphragm with an externally
>>> applied voltage field, then letting go. The resulting step response
>>> can be FFT'd into a frequency response that will be highly accurate.
>>> Of course this is an integrated response - there are still the polar
>>> irregularities to consider, but they too can be analysed
>>> geometrically.
>>>
>>> d
>> FFTs, in the 40's? I think not.
>>
> Have you never worked an FFT by hand?
>
> d
My error. I always thought Tukey and Cooley invented the
algorithm in the 60's.
A little checking and it seems Gauss came up with it
in the early 1800's. But no, I've never done an FFT by
hand other than a class room exercise.

Then too, I spent most of my career doing physical
chemistry. Not much call for FFTs there.

Later...
Ron Capik
--

Later...
Ron Capik
--

Don Pearce[_3_]

December 22nd 10, 04:54 PM

On Wed, 22 Dec 2010 08:42:34 -0800, "William Sommerwerck"
> wrote:

>> At least with a capacitor microphone there is a purely electrical
>> method, which involves deflecting the diaphragm with an externally
>> applied voltage field, then letting go. The resulting step response
>> can be FFT'd into a frequency response that will be highly accurate.
>> Of course this is an integrated response -- there are still the polar
>> irregularities to consider, but they too can be analysed
>> geometrically.
>
>The modern FFT did not exist until ca. 1967. I remember the IEEE article
>about it. It was A Big Thing.
>
>I don't see why the same sort of analysis could not be applied to a ribbon
>or dynamic mic.
>
You could certainly move a ribbon this way, but the electrically
induced pulse would be far bigger than the mechanical one. A capacitor
mic is electrically shielded so this method works well.

You are right about the date, of course, but I was not thinking in
terms of back-then, just making the general comment about a method.

d

Don Pearce[_3_]

December 22nd 10, 05:06 PM

On Wed, 22 Dec 2010 11:52:16 -0500, Ron Capik >
wrote:

>On 12/22/2010 10:49 AM, Don Pearce wrote:
>> On Wed, 22 Dec 2010 10:40:47 -0500, Ron >
>> wrote:
>>
>>> On 12/22/2010 9:57 AM, Don Pearce wrote:
>>>> On Wed, 22 Dec 2010 08:13:09 -0500, "Arny >
>>>> wrote:
>>>>
>>>>> "Don > wrote in message
>>>>>
>>>>>
>>>>>> There is a big chicken and egg question here, though. To
>>>>>> assess a microphone you need a better speaker.
>>>>>
>>>>> Other sonic sources such as spark gaps exist.
>>>>>
>>>>
>>>> I've done spark gap tests of mics myself, and posted the results here.
>>>> Their problem is that they are only good for the top end of the
>>>> response range.
>>>>
>>>>>> To assess
>>>>>> a speaker you need a better microphone. The BBC R&D
>>>>>> department managed to find their way into some
>>>>>> first-principles methods to design and assess their
>>>>>> microphones without benefit of suitable speakers. That
>>>>>> speaks of great theoretical capability as well as superb
>>>>>> engineering.
>>>>>
>>>>> Speakers used for calibrating microphones need only produce a usable
>>>>> response on axis, and at relatively low levels.
>>>>>
>>>>> Microphone calibrators are essentially special purpose speakers.
>>>>>
>>>>
>>>> Yes, but the chicken and egg question is not addressed by any of
>>>> these.
>>>>
>>>>> In the US, RCA and the AT&T labs were leaders in the use of first-principles
>>>>> means for evaluating and calibrating audio equipment including microphones.
>>>>>
>>>>
>>>> At least with a capacitor microphone there is a purely electrical
>>>> method, which involves deflecting the diaphragm with an externally
>>>> applied voltage field, then letting go. The resulting step response
>>>> can be FFT'd into a frequency response that will be highly accurate.
>>>> Of course this is an integrated response - there are still the polar
>>>> irregularities to consider, but they too can be analysed
>>>> geometrically.
>>>>
>>>> d
>>> FFTs, in the 40's? I think not.
>>>
>> Have you never worked an FFT by hand?
>>
>> d
>My error. I always thought Tukey and Cooley invented the
>algorithm in the 60's.
>A little checking and it seems Gauss came up with it
>in the early 1800's. But no, I've never done an FFT by
>hand other than a class room exercise.
>
>Then too, I spent most of my career doing physical
>chemistry. Not much call for FFTs there.
>

Fourier invented the technique for analysing heat transfer round an
iron ring. Buggered if I know why, though.

d

Kulin Remailer

December 22nd 10, 05:21 PM

> >> To assess

There's no need to be rude.

> there are still the polar irregularities to consider, but they too can be
> analysed geometrically.

And treated medically.

Scott Dorsey

December 22nd 10, 06:17 PM

William Sommerwerck > wrote:
>> At least with a capacitor microphone there is a purely electrical
>> method, which involves deflecting the diaphragm with an externally
>> applied voltage field, then letting go. The resulting step response
>> can be FFT'd into a frequency response that will be highly accurate.
>> Of course this is an integrated response -- there are still the polar
>> irregularities to consider, but they too can be analysed
>> geometrically.
>
>The modern FFT did not exist until ca. 1967. I remember the IEEE article
>about it. It was A Big Thing.

You don't really need the FFT to do any of this though it sure helps.

>I don't see why the same sort of analysis could not be applied to a ribbon
>or dynamic mic.

In the case of the dynamic, the diaphragm isn't conductive. In the case
of the ribbon, it's actually possible to do but difficult to isolate the
electrically-induced noise from the physical stuff.

In the case of the ribbon, though, many of the real response problems
don't have to do with the ribbon itself but with all of the magnets and
supports and crap around the ribbon. The guys at Sennheiser have got a
shockingly accurate math model of the ribbon itself, finally. The rest
of the stuff around it is a problem.
--scott
--
"C'est un Nagra. C'est suisse, et tres, tres precis."

Don Pearce[_3_]

December 22nd 10, 06:23 PM

On 22 Dec 2010 13:17:11 -0500, (Scott Dorsey) wrote:

>William Sommerwerck > wrote:
>>> At least with a capacitor microphone there is a purely electrical
>>> method, which involves deflecting the diaphragm with an externally
>>> applied voltage field, then letting go. The resulting step response
>>> can be FFT'd into a frequency response that will be highly accurate.
>>> Of course this is an integrated response -- there are still the polar
>>> irregularities to consider, but they too can be analysed
>>> geometrically.
>>
>>The modern FFT did not exist until ca. 1967. I remember the IEEE article
>>about it. It was A Big Thing.
>
>You don't really need the FFT to do any of this though it sure helps.
>
>>I don't see why the same sort of analysis could not be applied to a ribbon
>>or dynamic mic.
>
>In the case of the dynamic, the diaphragm isn't conductive. In the case
>of the ribbon, it's actually possible to do but difficult to isolate the
>electrically-induced noise from the physical stuff.
>
>In the case of the ribbon, though, many of the real response problems
>don't have to do with the ribbon itself but with all of the magnets and
>supports and crap around the ribbon. The guys at Sennheiser have got a
>shockingly accurate math model of the ribbon itself, finally. The rest
>of the stuff around it is a problem.

A problem, sure, but one that is perfectly susceptible to finite
element analysis - given a powerful enough computer and a few hours,
of course.

d

Scott Dorsey

December 22nd 10, 06:24 PM

Ron Capik > wrote:
>My error. I always thought Tukey and Cooley invented the
>algorithm in the 60's.

They did.

>A little checking and it seems Gauss came up with it
>in the early 1800's. But no, I've never done an FFT by
>hand other than a class room exercise.

Gauss's method used to be standard when I was in school. At some point the
IBM Scientific Subroutines Package came out with an update and the FOURIER
subroutine suddenly said "OLD SLOW METHOD: DO NOT USE." That's when the
C-T FFT came out. The FFT didn't do anything that Gauss' method didn't do,
it just did it amazingly faster. So amazingly faster that analyses that
had previously been impractical became routine.

For a long time they used to teach the Heaviside Calculus in EE. The
Heaviside Calculus was developed around 1910 or so and provides a somewhat
different notation for breaking complex waveforms up into components;
it's a lot easier to do by hand than the FFT is, but it pretty much fell
out of favor in the seventies.
--scott
--
"C'est un Nagra. C'est suisse, et tres, tres precis."

Scott Dorsey

December 22nd 10, 06:31 PM

Don Pearce > wrote:
>On 22 Dec 2010 13:17:11 -0500, (Scott Dorsey) wrote:
>>
>>In the case of the ribbon, though, many of the real response problems
>>don't have to do with the ribbon itself but with all of the magnets and
>>supports and crap around the ribbon. The guys at Sennheiser have got a
>>shockingly accurate math model of the ribbon itself, finally. The rest
>>of the stuff around it is a problem.
>
>A problem, sure, but one that is perfectly susceptible to finite
>element analysis - given a powerful enough computer and a few hours,
>of course.

Yup, that's the next step after the recent analysis from the Sennheiser
folks. Do it decently and it should be a shoe-in for a JAES paper.
--scott

--
"C'est un Nagra. C'est suisse, et tres, tres precis."

Don Pearce[_3_]

December 22nd 10, 06:36 PM

On 22 Dec 2010 13:31:35 -0500, (Scott Dorsey) wrote:

>Don Pearce > wrote:
>>On 22 Dec 2010 13:17:11 -0500, (Scott Dorsey) wrote:
>>>
>>>In the case of the ribbon, though, many of the real response problems
>>>don't have to do with the ribbon itself but with all of the magnets and
>>>supports and crap around the ribbon. The guys at Sennheiser have got a
>>>shockingly accurate math model of the ribbon itself, finally. The rest
>>>of the stuff around it is a problem.
>>
>>A problem, sure, but one that is perfectly susceptible to finite
>>element analysis - given a powerful enough computer and a few hours,
>>of course.
>
>Yup, that's the next step after the recent analysis from the Sennheiser
>folks. Do it decently and it should be a shoe-in for a JAES paper.

It's all in the mesh generation. Get that right and it works well. I
use it often in waveguide filter design. The corners are the thing.

d

William Sommerwerck

December 22nd 10, 07:10 PM

> Fourier invented the technique for analysing heat transfer
> round an iron ring. Buggered if I know why, though.

Fourier did not invent the FFT. He realized that any cyclic pattern could be
represented by a fundamental frequency, plus harmonics. The coefficients
would likely have been calculated using the integral calculus.

Don Pearce[_3_]

December 22nd 10, 07:16 PM

On Wed, 22 Dec 2010 11:10:17 -0800, "William Sommerwerck"
> wrote:

>> Fourier invented the technique for analysing heat transfer
>> round an iron ring. Buggered if I know why, though.
>
>Fourier did not invent the FFT. He realized that any cyclic pattern could be
>represented by a fundamental frequency, plus harmonics. The coefficients
>would likely have been calculated using the integral calculus.
>

Of course, I meant Fourier analysis, not the FFT which is a much later
invention.

d

Scott Dorsey

December 22nd 10, 07:23 PM

Don Pearce > wrote:
>On Wed, 22 Dec 2010 11:10:17 -0800, "William Sommerwerck"
> wrote:
>
>>> Fourier invented the technique for analysing heat transfer
>>> round an iron ring. Buggered if I know why, though.
>>
>>Fourier did not invent the FFT. He realized that any cyclic pattern could be
>>represented by a fundamental frequency, plus harmonics. The coefficients
>>would likely have been calculated using the integral calculus.
>
>Of course, I meant Fourier analysis, not the FFT which is a much later
>invention.

Fourier didn't even invent Fourier analysis... he invented this really
ingenious way of representing arbitrary function, considered it an
interesting curiosity... and there it sat really until the mid-20th century.
--scott
--
"C'est un Nagra. C'est suisse, et tres, tres precis."

Don Pearce[_3_]

December 22nd 10, 07:27 PM

On 22 Dec 2010 14:23:53 -0500, (Scott Dorsey) wrote:

>Don Pearce > wrote:
>>On Wed, 22 Dec 2010 11:10:17 -0800, "William Sommerwerck"
> wrote:
>>
>>>> Fourier invented the technique for analysing heat transfer
>>>> round an iron ring. Buggered if I know why, though.
>>>
>>>Fourier did not invent the FFT. He realized that any cyclic pattern could be
>>>represented by a fundamental frequency, plus harmonics. The coefficients
>>>would likely have been calculated using the integral calculus.
>>
>>Of course, I meant Fourier analysis, not the FFT which is a much later
>>invention.
>
>Fourier didn't even invent Fourier analysis... he invented this really
>ingenious way of representing arbitrary function, considered it an
>interesting curiosity... and there it sat really until the mid-20th century.

Really? Didn't know that.

d

Arny Krueger

December 22nd 10, 07:36 PM

"Don Pearce" > wrote in message

> On Wed, 22 Dec 2010 10:40:47 -0500, Ron Capik
> > wrote:
>
>> On 12/22/2010 9:57 AM, Don Pearce wrote:
>>> On Wed, 22 Dec 2010 08:13:09 -0500, "Arny
>>> > wrote:
>>>
>>>> "Don > wrote in message
>>>>
>>>>
>>>>> There is a big chicken and egg question here, though.
>>>>> To assess a microphone you need a better speaker.
>>>>
>>>> Other sonic sources such as spark gaps exist.
>>>>
>>>
>>> I've done spark gap tests of mics myself, and posted
>>> the results here. Their problem is that they are only
>>> good for the top end of the response range.
>>>
>>>>> To assess
>>>>> a speaker you need a better microphone. The BBC R&D
>>>>> department managed to find their way into some
>>>>> first-principles methods to design and assess their
>>>>> microphones without benefit of suitable speakers. That
>>>>> speaks of great theoretical capability as well as
>>>>> superb engineering.
>>>>
>>>> Speakers used for calibrating microphones need only
>>>> produce a usable response on axis, and at relatively
>>>> low levels.
>>>>
>>>> Microphone calibrators are essentially special purpose
>>>> speakers.
>>>>
>>>
>>> Yes, but the chicken and egg question is not addressed
>>> by any of these.
>>>
>>>> In the US, RCA and the AT&T labs were leaders in the
>>>> use of first-principles means for evaluating and
>>>> calibrating audio equipment including microphones.
>>>>
>>>
>>> At least with a capacitor microphone there is a purely
>>> electrical method, which involves deflecting the
>>> diaphragm with an externally applied voltage field,
>>> then letting go. The resulting step response can be
>>> FFT'd into a frequency response that will be highly
>>> accurate. Of course this is an integrated response -
>>> there are still the polar irregularities to consider,
>>> but they too can be analysed geometrically.
>>>
>>> d
>> FFTs, in the 40's? I think not.
>>
> Have you never worked an FFT by hand?

I think he's referring to the date of Cooley and Tuckey's seminal paper
(1965) that re-introduced Gauss's FFT technology (1805).

http://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm

Arny Krueger

December 22nd 10, 07:37 PM

"Don Pearce" > wrote in message

> On Wed, 22 Dec 2010 11:10:17 -0800, "William Sommerwerck"
> > wrote:
>
>>> Fourier invented the technique for analysing heat
>>> transfer round an iron ring. Buggered if I know why,
>>> though.
>>
>> Fourier did not invent the FFT. He realized that any
>> cyclic pattern could be represented by a fundamental
>> frequency, plus harmonics. The coefficients would likely
>> have been calculated using the integral calculus.
>>
>
> Of course, I meant Fourier analysis, not the FFT which is
> a much later invention.

The Fourier integrals could be evaluated by numerical means without the use
of digital technology or automatic computers.

William Sommerwerck

December 22nd 10, 09:04 PM

>>> Fourier did not invent the FFT. He realized that any cyclic pattern
could be
>>> represented by a fundamental frequency, plus harmonics. The coefficients
>>> would likely have been calculated using the integral calculus.

> >Of course, I meant Fourier analysis, not the FFT which is a much later
> >invention.

> Fourier didn't even invent Fourier analysis... he invented this really
> ingenious way of representing arbitrary function, considered it an
> interesting curiosity... and there it sat really until the mid-20th
century.

I think you're confusing Fourier analysis with the FFT.

If you can represent a cycle function analytically -- that is, as a
mathematical expression -- then you can extract the Fourier coefficients
simply by multiplying the function by the sine and cosine of the desired
harmonic, then integrating. I assume Fourier had figured this out, because
it's pretty basic stuff once you recognize what's going on.

Don Pearce[_3_]

December 22nd 10, 09:21 PM

On Wed, 22 Dec 2010 13:04:48 -0800, "William Sommerwerck"
> wrote:

>>>> Fourier did not invent the FFT. He realized that any cyclic pattern
>could be
>>>> represented by a fundamental frequency, plus harmonics. The coefficients
>>>> would likely have been calculated using the integral calculus.
>
>> >Of course, I meant Fourier analysis, not the FFT which is a much later
>> >invention.
>
>> Fourier didn't even invent Fourier analysis... he invented this really
>> ingenious way of representing arbitrary function, considered it an
>> interesting curiosity... and there it sat really until the mid-20th
>century.
>
>I think you're confusing Fourier analysis with the FFT.
>
>If you can represent a cycle function analytically -- that is, as a
>mathematical expression -- then you can extract the Fourier coefficients
>simply by multiplying the function by the sine and cosine of the desired
>harmonic, then integrating. I assume Fourier had figured this out, because
>it's pretty basic stuff once you recognize what's going on.
>

But it was Dirichlet who formalised it and defined exactly how, and
in what circumstances you could apply the method.

d

Scott Dorsey

December 22nd 10, 10:54 PM

William Sommerwerck > wrote:
>If you can represent a cycle function analytically -- that is, as a
>mathematical expression -- then you can extract the Fourier coefficients
>simply by multiplying the function by the sine and cosine of the desired
>harmonic, then integrating. I assume Fourier had figured this out, because
>it's pretty basic stuff once you recognize what's going on.

Yes! But Fourier never tried to apply this to any real-world applications,
and in fact nobody did until his work was rediscovered in the 20th century.
And by the time it was rediscovered, the Heaviside Calculus was already in
common use among engineers.
--scott
--
"C'est un Nagra. C'est suisse, et tres, tres precis."

Mark

December 23rd 10, 01:13 AM

On Dec 22, 5:54*pm, (Scott Dorsey) wrote:
> William Sommerwerck > wrote:
> >If you can represent a cycle function analytically -- that is, as a
> >mathematical expression -- then you can extract the Fourier coefficients
> >simply by multiplying the function by the sine and cosine of the desired
> >harmonic, then integrating. I assume Fourier had figured this out, because
> >it's pretty basic stuff once you recognize what's going on.
>

Fourier analysis was used before the FFT (FAST Fourier transform).
Every spectrum analyzer is a form of Fourier analysis.

C&T developed the FFT algorithm (the first F stands for FAST) which
made Fourier analysis practical to be computed with a digital computer
and DSP.

Mark

William Sommerwerck

December 23rd 10, 01:43 AM

> Every spectrum analyzer is [sic] a form of Fourier analysis.

No so. Spectrum analyzers are for random or non-repetitive signals, and do
not use Fourier analysis.

Mark

December 23rd 10, 02:36 AM

On Dec 22, 8:43*pm, "William Sommerwerck" >
wrote:
> > Every spectrum analyzer is [sic] a form of Fourier analysis.
>
> No so. Spectrum analyzers are for random or non-repetitive signals, and do
> not use Fourier analysis.

Gee, you have never seen the spectrum of a sine wave on a spectrum
analyzer?

Have a good day.

Mark

Scott Dorsey

December 23rd 10, 02:44 AM

Mark > wrote:
>On Dec 22, 8:43=A0pm, "William Sommerwerck" >
>wrote:
>> > Every spectrum analyzer is [sic] a form of Fourier analysis.
>>
>> No so. Spectrum analyzers are for random or non-repetitive signals, and d=
>o
>> not use Fourier analysis.
>
>Gee, you have never seen the spectrum of a sine wave on a spectrum
>analyzer?

Yup, but I have also seen the spectrum of an aperiodic signal on a spectrum
analyzer coming up with something utterly unlike what a Fourier analysis
would.

The spectrum analyzer is swept-sine and consequently if a frequency
appears in the input at any time other than the moment when the window
is directly on that frequency, it will not appear on the display.

This is an absolute and total nightmare for people trying to look at
WiFi signals with a regular spectrum analyzer, for instance. And it
is going to be a similar nightmare with the new "white space" devices
that will be sharing RF bandwidth with wireless mikes soon.
--scott

--
"C'est un Nagra. C'est suisse, et tres, tres precis."

alex

December 23rd 10, 03:02 AM

Il 22/12/2010 3.37, muzician21 ha scritto:
> I've read that certain vintage mics say from the 40's are considered
> desirable for their sound quality. So how did engineers of the day
> gauge the performance of mics if there wasn't a truly high quality
> playback system available?
>
> Or is that not correct?
exactly as they do today... Because the price! ;-)

alex

Anahata

December 23rd 10, 09:38 AM

On Wed, 22 Dec 2010 15:49:00 +0000, Don Pearce wrote:

> Have you never worked an FFT by hand?

That would be a SFT, then :-)

--
Anahata
--/-- http://www.treewind.co.uk
+44 (0)1638 720444

Don Pearce[_3_]

December 23rd 10, 09:47 AM

On Thu, 23 Dec 2010 03:38:04 -0600, anahata >
wrote:

>On Wed, 22 Dec 2010 15:49:00 +0000, Don Pearce wrote:
>
>> Have you never worked an FFT by hand?
>
>That would be a SFT, then :-)

Not to mention a very small one. 128 points was my limit one very
boring day.

d

Kevin Aylward[_4_]

December 23rd 10, 11:47 AM

>"Scott Dorsey" wrote in message ...

>William Sommerwerck > wrote:
>>If you can represent a cycle function analytically -- that is, as a
>>mathematical expression -- then you can extract the Fourier coefficients
>>simply by multiplying the function by the sine and cosine of the desired
>>harmonic, then integrating. I assume Fourier had figured this out, because
>>it's pretty basic stuff once you recognize what's going on.

>Yes! But Fourier never tried to apply this to any real-world applications,

This depends on your definition of applying to real world problems. I would
say that the general understanding is that Fourier specifically invented the
technique to solve real physical heat conduction and vibration problems, all
of which are meaningless in the virtual world. Fourier was apparently
working on actual physical problems, not abstract methods to solve
differential equations just for the sake of solving them. The understanding
was that Fourier got results that agreed with experiments, but that at the
time, were considered mathematically dubious. As others have already noted,
the mathematical justifications for the physical results came later.

Kevin Aylward B.Sc.
www.kevinaylward.co.uk

William Sommerwerck

December 23rd 10, 03:03 PM

>>> Every spectrum analyzer is [sic] a form of Fourier analysis.

>> No so. Spectrum analyzers are for random or non-repetitive
>> signals, and do not use Fourier analysis.

> Gee, you have never seen the spectrum of a sine wave on
> a spectrum analyzer?

Simply because a device analyzes the spectral content of a signal, doesn't
mean it uses Fourier analysis. Instead of arguing about it, look it up.

Mark

December 23rd 10, 05:43 PM

On Dec 23, 10:03*am, "William Sommerwerck"
> wrote:
> >>> Every spectrum analyzer is [sic] a form of Fourier analysis.

> >> No so. Spectrum analyzers are for random or non-repetitive
> >> signals, and do not use Fourier analysis.

> > Gee, you have never seen the spectrum of a sine wave on
> > a spectrum analyzer?
>
> Simply because a device analyzes the spectral content of a signal, doesn't
> mean it uses Fourier analysis. Instead of arguing about it, look it up.

Semantics about the word USES..

An HP8591 __USES__ swept filters to perform a Fourier analysis i.e.
convert a time domain signal to the frequency domain.

RTA software __USES__ an FFT algorithm to perform a Fourier analysis
i.e. convert a time domain signal to the frequency domain.

Spectral analysis = Fourier analysis = conversion between time and
frequency domains regardless of the method used.

The Fourier transform is one particular method to do this
mathematically.

The Fast Fourier transform is one particularly efficeint method to do
this mathematically.

http://en.wikipedia.org/wiki/Fourier_analysis

Have a nice day.

Mark

Scott Dorsey

December 23rd 10, 05:47 PM

Mark > wrote:
>
>An HP8591 __USES__ swept filters to perform a Fourier analysis i.e.
>convert a time domain signal to the frequency domain.

NOT all ways of converting a time domain signal to the frequency domain
are strictly speaking Fourier analysis.

>RTA software __USES__ an FFT algorithm to perform a Fourier analysis
>i.e. convert a time domain signal to the frequency domain.

And they both get the same value on a periodic input but different values
on aperiodic inputs. Consequently they are not performing the same function.

>Spectral analysis =3D Fourier analysis =3D conversion between time and
>frequency domains regardless of the method used.

Fourier analysis is one element of the set of spectral analyses.
--scott

--
"C'est un Nagra. C'est suisse, et tres, tres precis."

dbd

December 23rd 10, 06:03 PM

On Dec 22, 6:44*pm, (Scott Dorsey) wrote:
> ...

>
> Yup, but I have also seen the spectrum of an aperiodic signal on a spectrum
> analyzer coming up with something utterly unlike what a Fourier analysis
> would.
>
> The spectrum analyzer is swept-sine and consequently if a frequency
> appears in the input at any time other than the moment when the window
> is directly on that frequency, it will not appear on the display.
>
> This is an absolute and total nightmare for people trying to look at
> WiFi signals with a regular spectrum analyzer, for instance. *And it
> is going to be a similar nightmare with the new "white space" devices
> that will be sharing RF bandwidth with wireless mikes soon.
> --scott
> ...

Spectrum analyzers come in (at least ) two flavors: swept or
(super)heterodyne and realtime or dynamic signal analyzers. A number
of manufacturers of the former also produced the later such as
Tektronix and HP. The realtime or dynamic analyzers implement Fourier
methods.

See the Tektronix App Note 2EW_16550_0. A quick Google locates a copy
at:

http://www.isotest.es/web/Soporte/Formacion/Notas%20de%20aplicacion/TEKTRONIX/TEKTRONIX%20RSA/analisis%20en%20tiempo%20real%20para%20nuevas%20se %C3%B1ales.pdf

Also the Agilent App Note 243 or 5952-8898E available:

http://cp.literature.agilent.com/litweb/pdf/5952-8898E.pdf

Each of there references discusses both types of spectrum analyzer.

Dale B. Dalrymple

Scott Dorsey

December 23rd 10, 06:07 PM

dbd > wrote:
>Spectrum analyzers come in (at least ) two flavors: swept or
>(super)heterodyne and realtime or dynamic signal analyzers. A number
>of manufacturers of the former also produced the later such as
>Tektronix and HP. The realtime or dynamic analyzers implement Fourier
>methods.

Normally we call those DSAs or FFT analyzers to distinguish them from
regular swept spectrum analyzers.
--scott

--
"C'est un Nagra. C'est suisse, et tres, tres precis."

Don Pearce[_3_]

December 23rd 10, 06:19 PM

On 23 Dec 2010 13:07:23 -0500, (Scott Dorsey) wrote:

>dbd > wrote:
>>Spectrum analyzers come in (at least ) two flavors: swept or
>>(super)heterodyne and realtime or dynamic signal analyzers. A number
>>of manufacturers of the former also produced the later such as
>>Tektronix and HP. The realtime or dynamic analyzers implement Fourier
>>methods.
>
>Normally we call those DSAs or FFT analyzers to distinguish them from
>regular swept spectrum analyzers.
>--scott

And of course swept analyzers are realtime instruments.

d

William Sommerwerck

December 23rd 10, 06:27 PM

> An HP8591 __USES__ swept filters to perform a Fourier analysis
> i.e. convert a time domain signal to the frequency domain.

Wrong, wrong, wrong, wrong, wrong.

A conversion from the time domain to the frequency domain is not, per se, a
Fourier analysis. Some forms are; all are not.

Spectral analysis = Fourier analysis = conversion between time and
frequency domains regardless of the method used.

WRONG, WRONG, WRONG. See above.

Doug McDonald[_4_]

December 23rd 10, 06:30 PM

On 12/22/2010 7:43 PM, William Sommerwerck wrote:
>> Every spectrum analyzer is [sic] a form of Fourier analysis.
>
> No so. Spectrum analyzers are for random or non-repetitive signals, and do
> not use Fourier analysis.
>
>

Not necessarily so.

You can use short Fourier transforms for such purposes, and some
spectrum analyzers do so. What you can't do is use a single Fourier
transform over the who audio bandwidth and expect optimal time
resolution of the various channels at all frequencies.

Doug McDonald

dbd

December 23rd 10, 06:58 PM

On Dec 23, 10:07*am, (Scott Dorsey) wrote:
> dbd > wrote:
> >Spectrum analyzers come in (at least ) two flavors: swept or
> >(super)heterodyne and realtime or dynamic signal analyzers. A number
> >of manufacturers of the former also produced the later such as
> >Tektronix and HP. The realtime or dynamic analyzers implement Fourier
> >methods.
>
> Normally we call those DSAs or FFT analyzers to distinguish them from
> regular swept spectrum analyzers.
> --scott

The documentation shows that instrumentation manufacturers have long
made regular use of "spectrum analyzer" for all types. In the 1950's
the only spectrum analyzers may have been swept. Today the question of
which type is more common would produce a different idea of "regular".

Dale B. Dalrymple

Scott Dorsey

December 23rd 10, 08:59 PM

Doug McDonald > wrote:
>You can use short Fourier transforms for such purposes, and some
>spectrum analyzers do so. What you can't do is use a single Fourier
>transform over the who audio bandwidth and expect optimal time
>resolution of the various channels at all frequencies.

In the old days before Fourier analyzers, there were a couple solutions.
One was to have a box with dozens of parallel filters, and live with the
fairly wide bands that resulted. (This method is something I blame for
much of the horrible equalization in the seventies.)

The other, and the one that Bell Labs used for speech analysis, was to
make a tape loop and play a short sample back over and over, thereby
taking an aperiodic signal and making it periodic.
--scott
--
"C'est un Nagra. C'est suisse, et tres, tres precis."

Mark

December 23rd 10, 10:35 PM

>
> Also the Agilent App Note 243 or 5952-8898E available:
>
> http://cp.literature.agilent.com/litweb/pdf/5952-8898E.pdf
>
> Each of there references discusses both types of spectrum analyzer.
>

Yes I understand how the various types of spectrum analyzers operate.
Trust me, I do.

A prisim is an example a spectrum analyzer that operates on light.

And these devices ALL perform Fourier analysis even though they use
different methods.

The decomposition of a waveform into it's various sinusoidal
components is the defintion of Fourier analysis.

See page 7 of the paper you cited above:

http://cp.literature.agilent.com/litweb/pdf/5952-8898E.pdf

"It was shown over one hundred years ago by Baron Jean Baptiste
Fourier
that any waveform that exists in the real world can be generated by
adding up sine waves."

The decompostion of a waveform into it's sinusoidal components is
Fourier analysis regardless of method. If you disagree, it is a
matter of semantics and nothing more then that, so lets leave it at
that.

Mark

Arny Krueger

December 23rd 10, 11:24 PM

"Scott Dorsey" > wrote in message

> Mark > wrote:
>>
>> An HP8591 __USES__ swept filters to perform a Fourier
>> analysis i.e. convert a time domain signal to the
>> frequency domain.
>
> NOT all ways of converting a time domain signal to the
> frequency domain
> are strictly speaking Fourier analysis.
>
>> RTA software __USES__ an FFT algorithm to perform a
>> Fourier analysis i.e. convert a time domain signal to
>> the frequency domain.

> And they both get the same value on a periodic input but
> different values
> on aperiodic inputs. Consequently they are not
> performing the same function.

>> Spectral analysis =3D Fourier analysis =3D conversion
>> between time and frequency domains regardless of the
>> method used.
>
> Fourier analysis is one element of the set of spectral
> analyses. --scott

There have also long been audio spectrum analyzers that were based on banks
of narrow-band filters. This methodology was used in analog SS RTAs
starting back in the 1970s. Ivie, Rane, Gold Line, Audio Control, and
others sold them. Some were rack mounted and some were hand held.

In the 1960s I maintained military equipment that used this methodology to
analyze the spectrum of audio from Doppler radars. The tuned filters were
implemented with vacuum tube amplfiers and L-C networks. There were over 30
bands. The equipment was about the size of a good-sized kitchen oven, and
produced similar amounts of heat. This particular piece of equipment was
replaced by a minicomputer running FFT software in the 1970s.

Scott Dorsey

December 23rd 10, 11:35 PM

Arny Krueger > wrote:
>
>There have also long been audio spectrum analyzers that were based on banks
>of narrow-band filters. This methodology was used in analog SS RTAs
>starting back in the 1970s. Ivie, Rane, Gold Line, Audio Control, and
>others sold them. Some were rack mounted and some were hand held.

This is an RTA, realtime analyzer. It's not a spectrum analyzer, it is
a realtime analyzer because, unlike a spectrum analyzer, it displays
frequency vs. amplitude plots in real time.

>In the 1960s I maintained military equipment that used this methodology to
>analyze the spectrum of audio from Doppler radars. The tuned filters were
>implemented with vacuum tube amplfiers and L-C networks. There were over 30
>bands. The equipment was about the size of a good-sized kitchen oven, and
>produced similar amounts of heat. This particular piece of equipment was
>replaced by a minicomputer running FFT software in the 1970s.

Yup. Today the Watkins-Johnson folks make sigint systems that take
the RF input and knock down a 20 Mhz wide chunk of it into a 45 Mhz IF
signal, then digitize the whole damn thing at some crazy rate and tear it
up into narrow bands with FFTs in hardware. No oversampling and no
anti-aliasing filter save the IF filtration so strong signals nearby can
cause birdies.
--scott
--
"C'est un Nagra. C'est suisse, et tres, tres precis."

William Sommerwerck

December 23rd 10, 11:35 PM

> The decomposition of a waveform into its various sinusoidal
> components is the defintion of Fourier analysis.

That is not correct. It is the decomposition of the waveform into its
sinusoidal /harmonic/ components. Noise is made of sinusoidal components,
but they are not harmonically related, and their decomposition /is not/
Fourier analysis.

You really need to stop arguing for a bit, and look into this. You are quite
wrong.

William Sommerwerck

December 23rd 10, 11:40 PM

> Today the Watkins-Johnson folks make sigint systems
> that take the RF input and knock down a 20 Mhz wide
> chunk of it into a 45 Mhz IF signal, then digitize the whole
> damn thing at some crazy rate and tear it up into narrow
> bands with FFTs in hardware.

I believe that's incorrect.

I own the Sony DSP FM tuner, I doubt there's even one bit of FFT in its
algorithms. One does not need FFT to implement a bandpass filter. I don't
even see the connection.

david correia

December 24th 10, 12:37 AM

In article >,
Nomen Nescio > wrote:

> "...When I see you flo tin' down the gutter
> I'll give you uh bottle uh wine
> Put me on the white hook
> Back in the fat rack
> Shad rack ee shack
> The sumptin' hoop the sumptin' hoop
> The blimp the blimp
> The drazy hoops the drazy hoops
> They're camp they're camp
> Tits tits the blimp the blimp
> The mother ship the mother ship
> The brothers hid under their hood..."

Think the first time I heard it was on one them Warner Bros mail in a
buck or 2 compilation albums. You couldn't help but wonder as his voice
targeted your brain - what the **** is that!?

David Correia
www.Celebrationsound.com

Scott Dorsey

December 24th 10, 12:41 AM

William Sommerwerck > wrote:
>> Today the Watkins-Johnson folks make sigint systems
>> that take the RF input and knock down a 20 Mhz wide
>> chunk of it into a 45 Mhz IF signal, then digitize the whole
>> damn thing at some crazy rate and tear it up into narrow
>> bands with FFTs in hardware.
>
>I believe that's incorrect.
>
>I own the Sony DSP FM tuner, I doubt there's even one bit of FFT in its
>algorithms. One does not need FFT to implement a bandpass filter. I don't
>even see the connection.

Your Sony tuner probably just does some filtering and detection in
the digital domain, as well as probably reconstituting and decoding
IBOC FM digital subcarriers.

It doesn't need to provide a wide panadaptor display to show the
operator the realtime spectrum of all possible threats, and it doesn't
need to identify multiple signal sources using the same frequency either.
It also doesn't need to do pattern matching of the source spectrum and
correlate it against a database of possible threat sources. (Although
that might be great to help you identify unknown songs on the radio.)
Modern sigint receivers are a lot more sophisticated today than simple
communications receivers.
--scott

>
>

--
"C'est un Nagra. C'est suisse, et tres, tres precis."

Arny Krueger

December 24th 10, 12:42 AM

"William Sommerwerck" > wrote in
message

>> Today the Watkins-Johnson folks make sigint systems
>> that take the RF input and knock down a 20 Mhz wide
>> chunk of it into a 45 Mhz IF signal, then digitize the
>> whole damn thing at some crazy rate and tear it up into
>> narrow bands with FFTs in hardware.
>
> I believe that's incorrect.

I believe that Scott is correct, but you have to realize that he is talking
about a special-purpose analytical device, not a classic communications
receiver.

> I own the Sony DSP FM tuner, I doubt there's even one bit
> of FFT in its algorithms.

Yes, this device does use digital filtering, but there seems no need for th
simulated filter banks that FFT analysis is used to create.

> One does not need FFT to
> implement a bandpass filter. I don't even see the
> connection.

I agree with your analysis here, too. I don't see any need for a FFT in a
communications receiver, except perhaps as an extra-fancy tuning indicator.

Scott Dorsey

December 24th 10, 12:51 AM

Arny Krueger > wrote:
>
>I agree with your analysis here, too. I don't see any need for a FFT in a
>communications receiver, except perhaps as an extra-fancy tuning indicator.

There's a big need for extra-fancy tuning indicators for a lot of
applications. I have an old Heatkit SB-620 panadaptor display on my
ham receiver at home; it's just an ordinary if somewhat drifty spectrum
analyzer that displays the receiver IF using a swept oscillator of
somewhat doubtful linearity. It's fine for identifying signals
way down in the noise while rapidly scanning the bands, though, which
is what it's for.

At work I have a radio with an AOR SDU-5500 analyzer on it, which _is_
a real FFT and can not only display the IF spectrum in realtime but it
can do an accurate waterfall plot of time vs. frequency vs. amplitude.
This is an absolute necessity today for tracking down intermittent
interference to wireless mikes, because there are so many RF sources today
that just squit an occasional burst and don't get seen on a regular
spectrum analyzer.

There is an _enormous_ need for these things, and as RF interference
problems get worse, as people move more critical systems to unlicensed
shared ISM bands, and as consumer electronics manufacturers continue to
disregard Part 15 requirements and turn the whole RF spectrum into a
dumping ground, the need will be greater every day.
--scott
--
"C'est un Nagra. C'est suisse, et tres, tres precis."

dbd

December 24th 10, 01:04 AM

On Dec 23, 3:40*pm, "William Sommerwerck" >
wrote:
> > Today the Watkins-Johnson folks make sigint systems
> > that take the RF input and knock down a 20 Mhz wide
> > chunk of it into a 45 Mhz IF signal, then digitize the whole
> > damn thing at some crazy rate and tear it up into narrow
> > bands with FFTs in hardware.
>
> I believe that's incorrect.
>
> I own the Sony DSP FM tuner, I doubt there's even one bit of FFT in its
> algorithms. One does not need FFT to implement a bandpass filter. I don't
> even see the connection.

A Sony DSP FM tuner only needs 1 channel, not a full bank so the FFT
provides no speed advantage.

The definition of the calculated FFT coefficients is equivalent to a
bank of narrowband filters with the center frequencies translated to
DC. You can't do an FFT without calculating a bank of filters, what
you do with the coefficients is up to the user. The algorithm works
the same whether the signal fed to it consists of stationary sinusoids
or noise. The coefficients are better for determining the parameters
of the sinusoids than for determining the parameters of a noise
process, but the analysis is still a Fourier analysis.

Dale B. Dalrymple

William Sommerwerck

December 24th 10, 01:06 AM

> A prisim [sic] is an example a spectrum analyzer
> that operates on light.
> And these devices ALL perform Fourier analysis even
> though they use different methods.

A prism? Do you understand how a prism works? Of course not!

Mark

December 24th 10, 01:10 AM

On Dec 23, 6:35*pm, "William Sommerwerck" >
wrote:
> > The decomposition of a waveform into its various sinusoidal
> > components is the defintion of Fourier analysis.
>
> That is not correct. It is the decomposition of the waveform into its
> sinusoidal /harmonic/ components. Noise is made of sinusoidal components,
> but they are not harmonically related, and their decomposition /is not/
> Fourier analysis.
>
> You really need to stop arguing for a bit, and look into this. You are quite
> wrong.

it seems like you are saying that you believe that one cannot perform
a Fourier analysis of noise,

there is no point in continuing this..

Mark

William Sommerwerck

December 24th 10, 01:10 AM

>> I own the Sony DSP FM tuner, I doubt there's even one bit
>> of FFT in its algorithms. One does not need FFT to implement
>> a bandpass filter. I don't even see the connection.

> A Sony DSP FM tuner only needs 1 channel, not a full bank
> so the FFT provides no speed advantage.

It wouldn't even need FFT if 20 channels were needed!

> The definition of the calculated FFT coefficients is equivalent to a
> bank of narrowband filters with the center frequencies translated to
> DC. You can't do an FFT without calculating a bank of filters, what
> you do with the coefficients is up to the user.

An utter misconception of what's goingon.

> The algorithm works
> the same whether the signal fed to it consists of stationary sinusoids
> or noise. The coefficients are better for determining the parameters
> of the sinusoids than for determining the parameters of a noise
> process, but the analysis is still a Fourier analysis.

AAARRRGGGHHH!

It is amazing how people can utterly pervert the meaning of facts, then
insist they're right.

I get the feeling I'm arguing with people who think a Polaroid integral
print needs to be flapped to develop properly.

Doug McDonald[_7_]

December 24th 10, 01:52 AM

On 12/23/2010 6:41 PM, Scott Dorsey wrote:
> William > wrote:
>>> Today the Watkins-Johnson folks make sigint systems
>>> that take the RF input and knock down a 20 Mhz wide
>>> chunk of it into a 45 Mhz IF signal, then digitize the whole
>>> damn thing at some crazy rate and tear it up into narrow
>>> bands with FFTs in hardware.
>>
>> I believe that's incorrect.
>>
>> I own the Sony DSP FM tuner, I doubt there's even one bit of FFT in its
>> algorithms. One does not need FFT to implement a bandpass filter. I don't
>> even see the connection.
>
> Your Sony tuner probably just does some filtering and detection in
> the digital domain, as well as probably reconstituting and decoding
> IBOC FM digital subcarriers.
>
> It doesn't need to provide a wide panadaptor display to show the
> operator the realtime spectrum of all possible threats, and it doesn't
> need to identify multiple signal sources using the same frequency either.
> It also doesn't need to do pattern matching of the source spectrum and
> correlate it against a database of possible threat sources. (Although
> that might be great to help you identify unknown songs on the radio.)
> Modern sigint receivers are a lot more sophisticated today than simple
> communications receivers.

IBOC receivers are complicated software. IBOC is COFDM, and
receivers use FFT to decode it.

It may, however, not use FFT to decode plain FM.

Doug McDonald

Arny Krueger

December 24th 10, 03:34 AM

"William Sommerwerck" > wrote in
message

>> A prisim [sic] is an example a spectrum analyzer
>> that operates on light.
>> And these devices ALL perform Fourier analysis even
>> though they use different methods.

> A prism? Do you understand how a prism works? Of course
> not!

It turns out that the behavior of lenses and prisms can be analyzed using
spatial Fourier transforms and FFTs. One of the rather non-obvious things
that can be accomplished this way is refocusing an unfocused picture.

http://sharp.bu.edu/~slehar/fourier/fourier.html

Arny Krueger

December 24th 10, 03:41 AM

"William Sommerwerck" > wrote in
message
>>> I own the Sony DSP FM tuner, I doubt there's even one
>>> bit of FFT in its algorithms. One does not need FFT to
>>> implement a bandpass filter. I don't even see the
>>> connection.

>> A Sony DSP FM tuner only needs 1 channel, not a full bank
>> so the FFT provides no speed advantage.

> It wouldn't even need FFT if 20 channels were needed!

Which is not to say that there is no way to use FFT technology do it.

If I was going to build a receiver to monitor the entire FM band in a
certain locality, I might use a FFT-based receiver to do so.

>> The definition of the calculated FFT coefficients is
>> equivalent to a bank of narrowband filters with the
>> center frequencies translated to DC. You can't do an FFT
>> without calculating a bank of filters, what you do with
>> the coefficients is up to the user.
>
> An utter misconception of what's goingon.

FFT technology can be used to implement filters with more-or-less arbitrary
bandpass characteristics. FFT-based filters are commonly used in audio
production. For example Adobe Audition has two FFT-based filters, one that
implements the user's arbitrarily drawn frequency response curve and another
that implments the user's arbitrarily drawn phase response curve.

Arny Krueger

December 24th 10, 03:45 AM

"Scott Dorsey" > wrote in message

> Arny Krueger > wrote:
>>
>> I agree with your analysis here, too. I don't see any
>> need for a FFT in a communications receiver, except
>> perhaps as an extra-fancy tuning indicator.
>
> There's a big need for extra-fancy tuning indicators for
> a lot of applications. I have an old Heatkit SB-620
> panadaptor display on my
> ham receiver at home; it's just an ordinary if somewhat
> drifty spectrum analyzer that displays the receiver IF
> using a swept oscillator of somewhat doubtful linearity.
> It's fine for identifying signals
> way down in the noise while rapidly scanning the bands,
> though, which
> is what it's for.
>
> At work I have a radio with an AOR SDU-5500 analyzer on
> it, which _is_
> a real FFT and can not only display the IF spectrum in
> realtime but it can do an accurate waterfall plot of time
> vs. frequency vs. amplitude. This is an absolute
> necessity today for tracking down intermittent
> interference to wireless mikes, because there are so many
> RF sources today that just squit an occasional burst and
> don't get seen on a regular spectrum analyzer.
>
> There is an _enormous_ need for these things, and as RF
> interference problems get worse, as people move more
> critical systems to unlicensed shared ISM bands, and as
> consumer electronics manufacturers continue to disregard
> Part 15 requirements and turn the whole RF spectrum into
> a dumping ground, the need will be greater every day.
> --scott

Here's a modern, FFT-based approach to the same thing:

http://www.dxzone.com/cgi-bin/dir/jump2.cgi?ID=9270

"DL4YHF's Amateur Radio Software:
Audio Spectrum Analyzer ("Spectrum Lab"

"This program started as a simple FFT program running under DOS a long time
ago, but it is now a specialized audio analyzer, filter, frequency
converter, hum filter, data logger etc (see history at the bottom of this
page). You can download it from this site. Or look into the manual though
the manual included in the archive will be more up-to-date."

William Sommerwerck

December 24th 10, 03:50 AM

>> You really need to stop arguing for a bit, and look into this.
>> You are quite wrong.

> it seems like you are saying that you believe that one cannot
> perform a Fourier analysis of noise...

Uh... Noise does not generally have a harmonic structure.

> there is no point in continuing this.

Of course not. You might discover you were wrong.

William Sommerwerck

December 24th 10, 03:53 AM

"Arny Krueger" > wrote in message
...
> "William Sommerwerck" > wrote in
> message

>>> A prisim [sic] is an example a spectrum analyzer
>>> that operates on light.
>>> And these devices ALL perform Fourier analysis even
>>> though they use different methods.

>> A prism? Do you understand how a prism works? Of course
>> not!

> It turns out that the behavior of lenses and prisms can be analyzed using
> spatial Fourier transforms and FFTs. One of the rather non-obvious things
> that can be accomplished this way is refocusing an unfocused picture.
> http://sharp.bu.edu/~slehar/fourier/fourier.html

Arny, there is nothing whatever on this page about prisms. NOTHING.

I would be really curious to understand how optical dispersion can be
modeled as a cyclic function.

Don Pearce[_3_]

December 24th 10, 07:55 AM

On Thu, 23 Dec 2010 22:41:07 -0500, "Arny Krueger" >
wrote:

>"William Sommerwerck" > wrote in
>message
>>>> I own the Sony DSP FM tuner, I doubt there's even one
>>>> bit of FFT in its algorithms. One does not need FFT to
>>>> implement a bandpass filter. I don't even see the
>>>> connection.
>
>>> A Sony DSP FM tuner only needs 1 channel, not a full bank
>>> so the FFT provides no speed advantage.
>
>> It wouldn't even need FFT if 20 channels were needed!
>
>Which is not to say that there is no way to use FFT technology do it.
>
>If I was going to build a receiver to monitor the entire FM band in a
>certain locality, I might use a FFT-based receiver to do so.
>
>>> The definition of the calculated FFT coefficients is
>>> equivalent to a bank of narrowband filters with the
>>> center frequencies translated to DC. You can't do an FFT
>>> without calculating a bank of filters, what you do with
>>> the coefficients is up to the user.
>>
>> An utter misconception of what's goingon.
>
>FFT technology can be used to implement filters with more-or-less arbitrary
>bandpass characteristics. FFT-based filters are commonly used in audio
>production. For example Adobe Audition has two FFT-based filters, one that
>implements the user's arbitrarily drawn frequency response curve and another
>that implments the user's arbitrarily drawn phase response curve.
>

The question here is what gets FFT'd. I suspect that in the Audition
filters, the drawn curve is FFT'd into the time domain, then
convolution is used against the actual signal. Mathematically and
time-wise that would make much more sense than chopping the signal
into chunks, FFTing, multiplying by the filter function and IFFTing
back to time domain many, many times.

d

dbd

December 24th 10, 08:47 AM

On Dec 23, 5:10*pm, "William Sommerwerck" >
wrote:
>...
>
> > The definition of the calculated FFT coefficients is equivalent to a
> > bank of narrowband filters with the center frequencies translated to
> > DC. You can't do an FFT without calculating a bank of filters, what
> > you do with the coefficients is up to the user.
>
> An utter misconception of what's goingon.
>
I think you are simply ignorant of what is going on and the
applications and established practices. Filterbanks arejust another
useful valid interpretation of the DFT/FFT algorithm:

https://ccrma.stanford.edu/~jos/sasp/DFT_Filter_Bank_I.html

> > The algorithm works
> > the same whether the signal fed to it consists of stationary sinusoids
> > or noise. The coefficients are better for determining the parameters
> > of the sinusoids than for determining the parameters of a noise
> > process, but the analysis is still a Fourier analysis.
>
> AAARRRGGGHHH!

Noise measurement is one of the classic applications of the DFT:

https://ccrma.stanford.edu/~jos/st/Power_Spectral_Density_Estimation.html

>
> It is amazing how people can utterly pervert the meaning of facts, then
> insist they're right.
> ...

It's sad that you find such useful applications of the facts so
painful and confusing.

Dale B. Dalrymple

William Sommerwerck

December 24th 10, 10:08 AM

>> The definition of the calculated FFT coefficients is equivalent to a
>> bank of narrowband filters with the center frequencies translated to
>> DC. You can't do an FFT without calculating a bank of filters, what
>> you do with the coefficients is up to the user.

> An utter misconception of what's going on.

I think you are simply ignorant of what is going on and the
applications and established practices. Filterbanks arejust another
useful valid interpretation of the DFT/FFT algorithm:

https://ccrma.stanford.edu/~jos/sasp/DFT_Filter_Bank_I.html

>> The algorithm works
>> the same whether the signal fed to it consists of stationary sinusoids
>> or noise. The coefficients are better for determining the parameters
>> of the sinusoids than for determining the parameters of a noise
>> process, but the analysis is still a Fourier analysis.

> AAARRRGGGHHH!

Noise measurement is one of the classic applications of the DFT:

https://ccrma.stanford.edu/~jos/st/Power_Spectral_Density_Estimation.html

>
> It is amazing how people can utterly pervert the meaning of facts, then
> insist they're right.
> ...

It's sad that you find such useful applications of the facts so
painful and confusing.

I still say you don't understand what the Fourier transform is all about.
However, I can see how one might analyze a /time-limited/ noise signal.

William Sommerwerck

December 24th 10, 10:09 AM

"William Sommerwerck" > wrote in message
...
> >> You really need to stop arguing for a bit, and look into this.
> >> You are quite wrong.
>
> > it seems like you are saying that you believe that one cannot
> > perform a Fourier analysis of noise...
>
> Uh... Noise does not generally have a harmonic structure.
>
>
> > there is no point in continuing this.
>
> Of course not. You might discover you were wrong.

I partially retract what I said above.

Kevin Aylward[_4_]

December 24th 10, 12:45 PM

>The decomposition of a waveform into it's various sinusoidal
>components is the defintion of Fourier analysis.

>See page 7 of the paper you cited above:

>http://cp.literature.agilent.com/litweb/pdf/5952-8898E.pdf

>"It was shown over one hundred years ago by Baron Jean Baptiste Fourier
>that any waveform that exists in the real world can be generated by >adding
>up sine waves."

Technically, Fourier didn't actually prove that certain functions, under
certain conditions, could be expanded in a sine/cosine series. The proof
under what conditions such an orthogonal expansion is valid only came later
by others. Fourier is associated with using the technique to solve heat
conduction and vibration problems, but he was by no means the first to use
sine/cosine expansions of functions.

Secondly, it is not true that any waveform has a Fourier sine/cosine series
expansion. In general, only waveforms that are repetitive may be expanded in
such a manner. All real world signals are non repetitive, and can only be
approximated by repetitive waveforms. Non repetitive waveforms can be
analysed by Fourier Integral Transform methods.

Thirdly, there is a fundamental limit in knowing a signal's time domain
description and frequency domain description. It can be shown that sigma_T X
sigma_F >1/2. This means, that it is impossible to have complete knowledge
of both a signals time response and frequency response together.

Kevin Aylward B.Sc.
www.kevinaylward.co.uk

Kevin Aylward[_4_]

December 24th 10, 12:58 PM

"Mark" wrote in message
...

On Dec 23, 6:35 pm, "William Sommerwerck" >
wrote:
> > The decomposition of a waveform into its various sinusoidal
> > components is the defintion of Fourier analysis.
>
> That is not correct. It is the decomposition of the waveform into its
> sinusoidal /harmonic/ components. Noise is made of sinusoidal components,
> but they are not harmonically related, and their decomposition /is not/
> Fourier analysis.
>
> You really need to stop arguing for a bit, and look into this. You are
> quite
> wrong.

Not really.

>it seems like you are saying that you believe that one cannot perform a
>Fourier analysis of noise,
>there is no point in continuing this..

Depends on your definition of "Fourier analysis". In general, noise is not
periodic, therefore cannot be expanded in a Fourier sine/cosine series. Only
repetitive functions can be expanded as a Fourier sine/cosine series.

Noise may be analysed by Fourier Integral Transforms. Its very debatable
whether this is included under the term "Fourier Analysis". Technically,
generalising from a a discrete system to an continuous system can be very
problematic, mathematically.

Kevin Aylward B.Sc.
www.kevinaylward.co.uk

Don Pearce[_3_]

December 24th 10, 01:26 PM

On Fri, 24 Dec 2010 12:58:50 -0000, "Kevin Aylward"
> wrote:

>"Mark" wrote in message
...
>
>On Dec 23, 6:35 pm, "William Sommerwerck" >
>wrote:
>> > The decomposition of a waveform into its various sinusoidal
>> > components is the defintion of Fourier analysis.
>>
>> That is not correct. It is the decomposition of the waveform into its
>> sinusoidal /harmonic/ components. Noise is made of sinusoidal components,
>> but they are not harmonically related, and their decomposition /is not/
>> Fourier analysis.
>>
>> You really need to stop arguing for a bit, and look into this. You are
>> quite
>> wrong.
>
>Not really.
>
>>it seems like you are saying that you believe that one cannot perform a
>>Fourier analysis of noise,
>>there is no point in continuing this..
>
>Depends on your definition of "Fourier analysis". In general, noise is not
>periodic, therefore cannot be expanded in a Fourier sine/cosine series. Only
>repetitive functions can be expanded as a Fourier sine/cosine series.
>
>Noise may be analysed by Fourier Integral Transforms. Its very debatable
>whether this is included under the term "Fourier Analysis". Technically,
>generalising from a a discrete system to an continuous system can be very
>problematic, mathematically.
>
>
>Kevin Aylward B.Sc.
>www.kevinaylward.co.uk

You can certainly perform a Fourier analysis and get a result out. But
you do need to be aware of what it is you are looking at. What you are
seeing is the spectrum that would result if the noise were not random,
but an endless perfect repetition of the entire sample being analysed.
Given the right circumstances and signals, the result is correct. Get
things wrong and the result is nonsense, mitigated to an arbitrary
degree by selecting a suitable windowing function - a more or less
desperate attempt to make the cut ends join up neatly.

d

Arny Krueger

December 24th 10, 03:04 PM

"William Sommerwerck" > wrote in
message
> "Arny Krueger" > wrote in message
> ...
>> "William Sommerwerck" > wrote
>> in
>> message
>
>>>> A prisim [sic] is an example a spectrum analyzer
>>>> that operates on light.
>>>> And these devices ALL perform Fourier analysis even
>>>> though they use different methods.
>
>>> A prism? Do you understand how a prism works? Of course
>>> not!
>
>> It turns out that the behavior of lenses and prisms can
>> be analyzed using spatial Fourier transforms and FFTs.
>> One of the rather non-obvious things that can be
>> accomplished this way is refocusing an unfocused
>> picture.

>> http://sharp.bu.edu/~slehar/fourier/fourier.html

> Arny, there is nothing whatever on this page about
> prisms. NOTHING.

See the information about lenses?

A lens is a special case of the general class of items called prisms.
Fresnel lenses are even made out of prisms. Prisms and lenses both work on
the principle of refraction.

> I would be really curious to understand how optical
> dispersion can be modeled as a cyclic function.

I gave you a starting point, but the more general starting point is google.

Arny Krueger

December 24th 10, 03:11 PM

"Don Pearce" > wrote in message

> On Thu, 23 Dec 2010 22:41:07 -0500, "Arny Krueger"
> > wrote:

>> FFT technology can be used to implement filters with
>> more-or-less arbitrary bandpass characteristics.
>> FFT-based filters are commonly used in audio
>> production. For example Adobe Audition has two
>> FFT-based filters, one that implements the user's
>> arbitrarily drawn frequency response curve and another
>> that implments the user's arbitrarily drawn phase
>> response curve.

> The question here is what gets FFT'd.

Windowed sets of data.

> I suspect that in
> the Audition filters, the drawn curve is FFT'd into the
> time domain, then convolution is used against the actual
> signal.

Very little seems to be known about how Audition does much of anything at
that level of detail.

> Mathematically and time-wise that would make much
> more sense than chopping the signal into chunks, FFTing,
> multiplying by the filter function and IFFTing back to
> time domain many, many times.

Given that windowing and FFT size are known to be part of the processing,
the second method seems to be the more likely.

Don Pearce[_3_]

December 24th 10, 03:56 PM

On Fri, 24 Dec 2010 10:11:07 -0500, "Arny Krueger" >
wrote:

>"Don Pearce" > wrote in message

>> On Thu, 23 Dec 2010 22:41:07 -0500, "Arny Krueger"
>> > wrote:
>
>>> FFT technology can be used to implement filters with
>>> more-or-less arbitrary bandpass characteristics.
>>> FFT-based filters are commonly used in audio
>>> production. For example Adobe Audition has two
>>> FFT-based filters, one that implements the user's
>>> arbitrarily drawn frequency response curve and another
>>> that implments the user's arbitrarily drawn phase
>>> response curve.
>
>> The question here is what gets FFT'd.
>
>Windowed sets of data.
>
>> I suspect that in
>> the Audition filters, the drawn curve is FFT'd into the
>> time domain, then convolution is used against the actual
>> signal.
>
>Very little seems to be known about how Audition does much of anything at
>that level of detail.
>
>> Mathematically and time-wise that would make much
>> more sense than chopping the signal into chunks, FFTing,
>> multiplying by the filter function and IFFTing back to
>> time domain many, many times.
>
>Given that windowing and FFT size are known to be part of the processing,
>the second method seems to be the more likely.
>
>
>

Windowing is no good with audio when you have to turn it back into
time domain. You end up with amplitude modulation of the finished
waveform that way. I suspect that the filter response is IFFT'd then
convolved with the audio on the fly. That would use least processing,
and minimize latency in real-time filtering.

Of course the Audition FFT filter comes with the problem that
amplitude and phase responses are not related, so you can't get back
to where you started later using minimum phase networks.

d

William Sommerwerck

December 24th 10, 04:14 PM

> A lens is a special case of the general class of items
> called prisms. Fresnel lenses are even made out of prisms.
> Prisms and lenses both work on the principle of refraction.

If you're talking about prisms as devices that divide light into a spectrum,
the operating principle is dispersion, not refraction. (Granted, dispersion
is a subset of refraction.)

I know of no use of prisms as image-forming devices. They are not lenses.
(The ridges of a Fresnel lens are not prisms, but "onion ring" cores of a
lens surface.) They are commonly used -- particularly in binoculars and
SLRs -- to direct the light in a different direction /without "processing"
it/ in any way.

dbd

December 24th 10, 05:33 PM

On Dec 24, 2:08*am, "William Sommerwerck" >
wrote:

> ...
> I still say you don't understand what the Fourier transform is all about.
> However, I can see how one might analyze a /time-limited/ noise signal.

I understand that the continuous/infinite Fourier transform is
different from the finite/discrete operations we are limited to in the
real world. By understanding the differences we can restrain our
expectations from infinite/continuous theory to achievable finite/
sampled theory and practice. We can use the knowledge of the
differences to work around some of the limitations.

For finite noise analysis, the FFT estimate of power spectral density
has a high variance, so averaging of multiple estimates is used to
reduce the variance. If you expect the zero variance of the infinite/
continuous Fourier transform on infinite/continuous data from
calculations on finite sample sets it is your expectations that are
faulty. The variance does not come from a failure to perform a proper
Fourier analysis but from performing the Fourier analysis on a finite
set of samples. Unfortunately, we have not had time to collect any
infinite noise records for analysis.

Dale B. Dalrymple

William Sommerwerck

December 24th 10, 05:50 PM

> I still say you don't understand what the Fourier transform is all about.
> However, I can see how one might analyze a /time-limited/ noise signal.

I understand that the continuous/infinite Fourier transform is
different from the finite/discrete operations we are limited to in the
real world. By understanding the differences we can restrain our
expectations from infinite/continuous theory to achievable finite/
sampled theory and practice. We can use the knowledge of the
differences to work around some of the limitations.

For finite noise analysis, the FFT estimate of power spectral density
has a high variance, so averaging of multiple estimates is used to
reduce the variance. If you expect the zero variance of the infinite/
continuous Fourier transform on infinite/continuous data from
calculations on finite sample sets it is your expectations that are
faulty. The variance does not come from a failure to perform a proper
Fourier analysis but from performing the Fourier analysis on a finite
set of samples. Unfortunately, we have not had time to collect any
infinite noise records for analysis.

If the noise is a stochastic process (am I using the term correctly?), would
you need an "infinite" sample? And if not, what would be the minimum sample
length? (I assume it would be inversely proportional to the lowest frequency
you wanted to measure.)

dbd

December 24th 10, 08:18 PM

On Dec 24, 9:50*am, "William Sommerwerck" >
wrote:
> ...

>
> If the noise is a stochastic process (am I using the term correctly?), would
> you need an "infinite" sample? And if not, what would be the minimum sample
> length? (I assume it would be inversely proportional to the lowest frequency
> you wanted to measure.)

You cannot achieve perfect reconstruction of the stochastic noise
process with a finite set of samples. If you simply take the DFT of n
real samples you get n/2 independent Fourier coefficients to calculate
n/2 power spectral density (PSD) estimates. If you simply increase n,
you get more estimates at closer frequency spacing with the same high
variance. To reduce the variance, multiple independent sets of (small)
n real samples are used to generate PSD estimates that are averaged
across multiple blocks to reduce variance. The conventional DFT will
produce one of the estimates at DC. This was in the reference I gave
on noise processing.

For sums of sets of stationary tones you can achieve perfect
reconstruction only if all the tones belong to a set of n/2
frequencies evenly spaced on 0 to Fsample/2. With the conventional
DFT, one of the tones will be DC. If these conditions are met, you can
achieve perfect reconstruction anywhere on the region over which the
tones are stationary. These conditions are seldom met in real
instrumentation and with real-world signals

Fortunately it is not necessary to be able to achieve perfect
reconstruction to make useful applications of Fourier analysis of
finite/discrete data sets. Is it equivalent to infinite/continuous
Fourier analysis of infinite/continuous signals? No, it doesn't need
to be.

Dale B. Dalrymple

William Sommerwerck

December 24th 10, 08:59 PM

> If the noise is a stochastic process (am I using the term correctly?),
would
> you need an "infinite" sample? And if not, what would be the minimum
sample
> length? (I assume it would be inversely proportional to the lowest
frequency
> you wanted to measure.)

You cannot achieve perfect reconstruction of the stochastic noise
process with a finite set of samples. If you simply take the DFT of n
real samples you get n/2 independent Fourier coefficients to calculate
n/2 power spectral density (PSD) estimates. If you simply increase n,
you get more estimates at closer frequency spacing with the same high
variance. To reduce the variance, multiple independent sets of (small)
n real samples are used to generate PSD estimates that are averaged
across multiple blocks to reduce variance. The conventional DFT will
produce one of the estimates at DC. This was in the reference I gave
on noise processing.

For sums of sets of stationary tones you can achieve perfect
reconstruction only if all the tones belong to a set of n/2
frequencies evenly spaced on 0 to Fsample/2. With the conventional
DFT, one of the tones will be DC. If these conditions are met, you can
achieve perfect reconstruction anywhere on the region over which the
tones are stationary. These conditions are seldom met in real
instrumentation and with real-world signals

Fortunately it is not necessary to be able to achieve perfect
reconstruction to make useful applications of Fourier analysis of
finite/discrete data sets. Is it equivalent to infinite/continuous
Fourier analysis of infinite/continuous signals? No, it doesn't need
to be.

That pretty much makes sense.

Peter Larsen[_3_]

December 24th 10, 11:07 PM

muzician21 wrote:

> I've read that certain vintage mics say from the 40's are considered
> desirable for their sound quality. So how did engineers of the day
> gauge the performance of mics if there wasn't a truly high quality
> playback system available?

I keep wondering about the premise of this question.

> Or is that not correct?

Let us assume, just for brief moment, listening via say a 10" wideband
loudspeaker sans a whizzer cone. Such a unit would not be improbable back
then. Considering how many audio production differences and compression
artifacts that are audible on various car audio and workplace loudenboomer
systems I think it perfectly possible to hear the difference between more or
less clear impulse response on such a loudspeaker.

I think you need to allow for what we would consider acceptable quality
playback systems further back in time than your question implicitly asserts.
Also btw. it may make sense to just consider "transducer-evolution" since
loudspeaker and microphone technology goes hand in hand.

Kind regards

Peter Larsen

Arny Krueger

December 26th 10, 12:48 PM

"William Sommerwerck" > wrote in
message

>> A lens is a special case of the general class of items
>> called prisms. Fresnel lenses are even made out of
>> prisms. Prisms and lenses both work on the principle of
>> refraction.
>
> If you're talking about prisms as devices that divide
> light into a spectrum, the operating principle is
> dispersion, not refraction. (Granted, dispersion is a
> subset of refraction.)

I think that the obvious claim that refraction is not involved with prisms,
speaks for itself. Suffice it to say it must have been way too long since
you took high school physics, if you were conscious at the time.

> I know of no use of prisms as image-forming devices.

One word: Periscopes and binoculars.

> They are not lenses.

But they unambigiously function based on refraction.

http://library.thinkquest.org/22915/refraction.html (of literally thousands
of similar references).

> (The ridges of a Fresnel lens are not
> prisms, but "onion ring" cores of a lens surface.)

I've actually seen working fresnel lenses formed of prisms. You don't even
have to curve the segments if you make them up of small enough pieces. There
are rectangular fresnels that are used for lighting that are formed of
straight prismatic shapes.

It is quite clear that you'd proudly deny that you were born of a woman in
order to score points in a debate, William. This only reinforces
speculation about your canine orgins! ;-)

>They are commonly used -- particularly in binoculars and SLRs
> -- to direct the light in a different direction /without
> "processing" it/ in any way.

Let us know when you come to your senses, William.

Arny Krueger

December 26th 10, 12:50 PM

"Don Pearce" > wrote in message

> On Fri, 24 Dec 2010 10:11:07 -0500, "Arny Krueger"
> > wrote:
>
>> "Don Pearce" > wrote in message
>>
>>> On Thu, 23 Dec 2010 22:41:07 -0500, "Arny Krueger"
>>> > wrote:
>>
>>>> FFT technology can be used to implement filters with
>>>> more-or-less arbitrary bandpass characteristics.
>>>> FFT-based filters are commonly used in audio
>>>> production. For example Adobe Audition has two
>>>> FFT-based filters, one that implements the user's
>>>> arbitrarily drawn frequency response curve and another
>>>> that implments the user's arbitrarily drawn phase
>>>> response curve.
>>
>>> The question here is what gets FFT'd.
>>
>> Windowed sets of data.
>>
>>> I suspect that in
>>> the Audition filters, the drawn curve is FFT'd into the
>>> time domain, then convolution is used against the actual
>>> signal.
>>
>> Very little seems to be known about how Audition does
>> much of anything at that level of detail.
>>
>>> Mathematically and time-wise that would make much
>>> more sense than chopping the signal into chunks, FFTing,
>>> multiplying by the filter function and IFFTing back to
>>> time domain many, many times.
>>
>> Given that windowing and FFT size are known to be part
>> of the processing, the second method seems to be the
>> more likely.

> Windowing is no good with audio when you have to turn it
> back into time domain. You end up with amplitude
> modulation of the finished waveform that way.

Only if you do it incorrectly.

> I suspect
> that the filter response is IFFT'd then convolved with
> the audio on the fly. That would use least processing,
> and minimize latency in real-time filtering.

Prove it!

> Of course the Audition FFT filter comes with the problem
> that amplitude and phase responses are not related, so
> you can't get back to where you started later using
> minimum phase networks.

Since Audition also has minimum phase filters readily available, its all
about giving the user choices.

Don Pearce[_3_]

December 26th 10, 01:04 PM

On Sun, 26 Dec 2010 07:50:14 -0500, "Arny Krueger" >
wrote:

>"Don Pearce" > wrote in message

>> On Fri, 24 Dec 2010 10:11:07 -0500, "Arny Krueger"
>> > wrote:
>>
>>> "Don Pearce" > wrote in message
>>>
>>>> On Thu, 23 Dec 2010 22:41:07 -0500, "Arny Krueger"
>>>> > wrote:
>>>
>>>>> FFT technology can be used to implement filters with
>>>>> more-or-less arbitrary bandpass characteristics.
>>>>> FFT-based filters are commonly used in audio
>>>>> production. For example Adobe Audition has two
>>>>> FFT-based filters, one that implements the user's
>>>>> arbitrarily drawn frequency response curve and another
>>>>> that implments the user's arbitrarily drawn phase
>>>>> response curve.
>>>
>>>> The question here is what gets FFT'd.
>>>
>>> Windowed sets of data.
>>>
>>>> I suspect that in
>>>> the Audition filters, the drawn curve is FFT'd into the
>>>> time domain, then convolution is used against the actual
>>>> signal.
>>>
>>> Very little seems to be known about how Audition does
>>> much of anything at that level of detail.
>>>
>>>> Mathematically and time-wise that would make much
>>>> more sense than chopping the signal into chunks, FFTing,
>>>> multiplying by the filter function and IFFTing back to
>>>> time domain many, many times.
>>>
>>> Given that windowing and FFT size are known to be part
>>> of the processing, the second method seems to be the
>>> more likely.
>
>> Windowing is no good with audio when you have to turn it
>> back into time domain. You end up with amplitude
>> modulation of the finished waveform that way.
>
>Only if you do it incorrectly.
>
No, that is what windowing does. It reduces the amplitude of the
samples to zero in a controlled manner at the two ends. There is no
"correct" way to do it that doesn't modulate the amplitude.

>> I suspect
>> that the filter response is IFFT'd then convolved with
>> the audio on the fly. That would use least processing,
>> and minimize latency in real-time filtering.
>
>Prove it!
>
Prove what? That convolution on the fly is quicker, and has less
latency than taking groups of data, performing an FFT, multiplying,
performing an IFFT then moving on... do I really need to prove that?

>> Of course the Audition FFT filter comes with the problem
>> that amplitude and phase responses are not related, so
>> you can't get back to where you started later using
>> minimum phase networks.
>
>Since Audition also has minimum phase filters readily available, its all
>about giving the user choices.
>
Sure. I was just making the point that some may not have grasped about
an important aspect of the FFT filter.

d

William Sommerwerck

December 26th 10, 02:08 PM

>>> A lens is a special case of the general class of items
>>> called prisms. Fresnel lenses are even made out of
>>> prisms. Prisms and lenses both work on the principle
>>> of refraction.

>> If you're talking about prisms as devices that divide
>> light into a spectrum, the operating principle is
>> dispersion, not refraction. (Granted, dispersion is a
>> subset of refraction.)

> I think that the obvious claim that refraction is not involved with
> prisms, speaks for itself. Suffice it to say it must have been way
> too long since you took high school physics, if you were conscious
> at the time.

>> I know of no use of prisms as image-forming devices.

> One word: Periscopes and binoculars.

Sorry about that, but the prisms in periscopes and binoculars are not used
to form images. They simply redirect the light.

>> They are not lenses.

> But they unambigiously function based on refraction.

But that wasn't the point.

> http://library.thinkquest.org/22915/refraction.html (of literally
thousands
> of similar references).

>> (The ridges of a Fresnel lens are not
>> prisms, but "onion ring" cores of a lens surface.)

> I've actually seen working fresnel lenses formed of prisms. You don't even
> have to curve the segments if you make them up of small enough pieces.
There
> are rectangular fresnels that are used for lighting that are formed of
> straight prismatic shapes.

> It is quite clear that you'd proudly deny that you were born of a woman in
> order to score points in a debate, William. This only reinforces
> speculation about your canine orgins! ;-)

Bitch. (Couldn't resist that.)

>>They are commonly used -- particularly in binoculars and SLRs
>> -- to direct the light in a different direction /without
>> "processing" it/ in any way.

> Let us know when you come to your senses, William.

Let us know when you start understanding what you're talking about, rather
than repeating your misunderstanding of what you read.

Arny Krueger

December 27th 10, 12:23 PM

"Don Pearce" > wrote in message

> On Sun, 26 Dec 2010 07:50:14 -0500, "Arny Krueger"
> > wrote:
>
>> "Don Pearce" > wrote in message
>>
>>> On Fri, 24 Dec 2010 10:11:07 -0500, "Arny Krueger"
>>> > wrote:
>>>
>>>> "Don Pearce" > wrote in message
>>>>
>>>>> On Thu, 23 Dec 2010 22:41:07 -0500, "Arny Krueger"
>>>>> > wrote:
>>>>
>>>>>> FFT technology can be used to implement filters with
>>>>>> more-or-less arbitrary bandpass characteristics.
>>>>>> FFT-based filters are commonly used in audio
>>>>>> production. For example Adobe Audition has two
>>>>>> FFT-based filters, one that implements the user's
>>>>>> arbitrarily drawn frequency response curve and
>>>>>> another that implments the user's arbitrarily drawn
>>>>>> phase response curve.
>>>>
>>>>> The question here is what gets FFT'd.
>>>>
>>>> Windowed sets of data.
>>>>
>>>>> I suspect that in
>>>>> the Audition filters, the drawn curve is FFT'd into
>>>>> the time domain, then convolution is used against the
>>>>> actual signal.

Thus we establish what the context of the discusison is about - exactly what
processing scheme does Audition use when it does FFT filtering. Remember
this folks, as my correspondent seems to want to ditch it at his first
convenience.

>>>> Very little seems to be known about how Audition does
>>>> much of anything at that level of detail.
>
>>>>> Mathematically and time-wise that would make much
>>>>> more sense than chopping the signal into chunks,
>>>>> FFTing, multiplying by the filter function and
>>>>> IFFTing back to time domain many, many times.

>>>> Given that windowing and FFT size are known to be part
>>>> of the processing, the second method seems to be the
>>>> more likely.
>>
>>> Windowing is no good with audio when you have to turn it
>>> back into time domain. You end up with amplitude
>>> modulation of the finished waveform that way.

>> Only if you do it incorrectly.

> No, that is what windowing does. It reduces the amplitude
> of the samples to zero in a controlled manner at the two
> ends. There is no "correct" way to do it that doesn't
> modulate the amplitude.

Please step back and see the big picture. Eventually these filters produce a
continuous audio output signal. Is the output of the filter amplitude
modulated as a byproduct of the windowing or not?

>>> I suspect
>>> that the filter response is IFFT'd then convolved with
>>> the audio on the fly. That would use least processing,
>>> and minimize latency in real-time filtering.

>> Prove it!

> Prove what? That convolution on the fly is quicker, and
> has less latency than taking groups of data, performing
> an FFT, multiplying, performing an IFFT then moving on...
> do I really need to prove that?

Again, you are answering a question that you made up, not the one I asked.
The topic is how a particular piece of software does FFT filtering. You
either know or you are speculating.

>>> Of course the Audition FFT filter comes with the problem
>>> that amplitude and phase responses are not related, so
>>> you can't get back to where you started later using
>>> minimum phase networks.

>> Since Audition also has minimum phase filters readily
>> available, its all about giving the user choices.

> Sure. I was just making the point that some may not have
> grasped about an important aspect of the FFT filter.

Only in your dreams.

Don Pearce[_3_]

December 27th 10, 12:42 PM

On Mon, 27 Dec 2010 07:23:52 -0500, "Arny Krueger" >
wrote:

>"Don Pearce" > wrote in message

>> On Sun, 26 Dec 2010 07:50:14 -0500, "Arny Krueger"
>> > wrote:
>>
>>> "Don Pearce" > wrote in message
>>>
>>>> On Fri, 24 Dec 2010 10:11:07 -0500, "Arny Krueger"
>>>> > wrote:
>>>>
>>>>> "Don Pearce" > wrote in message
>>>>>
>>>>>> On Thu, 23 Dec 2010 22:41:07 -0500, "Arny Krueger"
>>>>>> > wrote:
>>>>>
>>>>>>> FFT technology can be used to implement filters with
>>>>>>> more-or-less arbitrary bandpass characteristics.
>>>>>>> FFT-based filters are commonly used in audio
>>>>>>> production. For example Adobe Audition has two
>>>>>>> FFT-based filters, one that implements the user's
>>>>>>> arbitrarily drawn frequency response curve and
>>>>>>> another that implments the user's arbitrarily drawn
>>>>>>> phase response curve.
>>>>>
>>>>>> The question here is what gets FFT'd.
>>>>>
>>>>> Windowed sets of data.
>>>>>
>>>>>> I suspect that in
>>>>>> the Audition filters, the drawn curve is FFT'd into
>>>>>> the time domain, then convolution is used against the
>>>>>> actual signal.
>
>Thus we establish what the context of the discusison is about - exactly what
>processing scheme does Audition use when it does FFT filtering. Remember
>this folks, as my correspondent seems to want to ditch it at his first
>convenience.
>
>>>>> Very little seems to be known about how Audition does
>>>>> much of anything at that level of detail.
>>
>>>>>> Mathematically and time-wise that would make much
>>>>>> more sense than chopping the signal into chunks,
>>>>>> FFTing, multiplying by the filter function and
>>>>>> IFFTing back to time domain many, many times.
>
>>>>> Given that windowing and FFT size are known to be part
>>>>> of the processing, the second method seems to be the
>>>>> more likely.
>>>
>>>> Windowing is no good with audio when you have to turn it
>>>> back into time domain. You end up with amplitude
>>>> modulation of the finished waveform that way.
>
>>> Only if you do it incorrectly.
>
>
>> No, that is what windowing does. It reduces the amplitude
>> of the samples to zero in a controlled manner at the two
>> ends. There is no "correct" way to do it that doesn't
>> modulate the amplitude.
>
>Please step back and see the big picture. Eventually these filters produce a
>continuous audio output signal. Is the output of the filter amplitude
>modulated as a byproduct of the windowing or not?

Clearly not, therefore there is no windowing. Windowing ALWAYS
modulates the amplitude - that is its function.

>
>>>> I suspect
>>>> that the filter response is IFFT'd then convolved with
>>>> the audio on the fly. That would use least processing,
>>>> and minimize latency in real-time filtering.
>
>>> Prove it!
>
>> Prove what? That convolution on the fly is quicker, and
>> has less latency than taking groups of data, performing
>> an FFT, multiplying, performing an IFFT then moving on...
>> do I really need to prove that?
>
>Again, you are answering a question that you made up, not the one I asked.
>The topic is how a particular piece of software does FFT filtering. You
>either know or you are speculating.
>

My statement, for which you demanded proof, was that convolution
against a timebased response was quicker than and FFT method which
demanded that the audio be cut into chunks. That was what you demanded
I prove. I decline. It is obvious.

>>>> Of course the Audition FFT filter comes with the problem
>>>> that amplitude and phase responses are not related, so
>>>> you can't get back to where you started later using
>>>> minimum phase networks.
>
>>> Since Audition also has minimum phase filters readily
>>> available, its all about giving the user choices.
>
>> Sure. I was just making the point that some may not have
>> grasped about an important aspect of the FFT filter.
>
>Only in your dreams.
>
You think that everybody in the world understands that FFT-based
filters do not exhibit a minimum phase relationship to their
time-based response? Did you over-indulge this Christmas?

d

Arny Krueger

December 27th 10, 12:54 PM

"Don Pearce" > wrote in message

> On Mon, 27 Dec 2010 07:23:52 -0500, "Arny Krueger"
> > wrote:
>
>> "Don Pearce" > wrote in message
>>
>>> On Sun, 26 Dec 2010 07:50:14 -0500, "Arny Krueger"
>>> > wrote:
>>>
>>>> "Don Pearce" > wrote in message
>>>>
>>>>> On Fri, 24 Dec 2010 10:11:07 -0500, "Arny Krueger"
>>>>> > wrote:
>>>>>
>>>>>> "Don Pearce" > wrote in message
>>>>>>
>>>>>>> On Thu, 23 Dec 2010 22:41:07 -0500, "Arny Krueger"
>>>>>>> > wrote:
>>>>>>
>>>>>>>> FFT technology can be used to implement filters
>>>>>>>> with more-or-less arbitrary bandpass
>>>>>>>> characteristics. FFT-based filters are commonly
>>>>>>>> used in audio production. For example Adobe
>>>>>>>> Audition has two FFT-based filters, one that
>>>>>>>> implements the user's arbitrarily drawn frequency
>>>>>>>> response curve and another that implments the
>>>>>>>> user's arbitrarily drawn phase response curve.
>>>>>>
>>>>>>> The question here is what gets FFT'd.
>>>>>>
>>>>>> Windowed sets of data.
>>>>>>
>>>>>>> I suspect that in
>>>>>>> the Audition filters, the drawn curve is FFT'd into
>>>>>>> the time domain, then convolution is used against
>>>>>>> the actual signal.
>>
>> Thus we establish what the context of the discusison is
>> about - exactly what processing scheme does Audition use
>> when it does FFT filtering. Remember this folks, as my
>> correspondent seems to want to ditch it at his first
>> convenience.
>>
>>>>>> Very little seems to be known about how Audition does
>>>>>> much of anything at that level of detail.
>>>
>>>>>>> Mathematically and time-wise that would make much
>>>>>>> more sense than chopping the signal into chunks,
>>>>>>> FFTing, multiplying by the filter function and
>>>>>>> IFFTing back to time domain many, many times.
>>
>>>>>> Given that windowing and FFT size are known to be
>>>>>> part of the processing, the second method seems to
>>>>>> be the more likely.
>>>>
>>>>> Windowing is no good with audio when you have to turn
>>>>> it back into time domain. You end up with amplitude
>>>>> modulation of the finished waveform that way.
>>
>>>> Only if you do it incorrectly.
>>
>>
>>> No, that is what windowing does. It reduces the
>>> amplitude of the samples to zero in a controlled manner
>>> at the two ends. There is no "correct" way to do it
>>> that doesn't modulate the amplitude.
>>
>> Please step back and see the big picture. Eventually
>> these filters produce a continuous audio output signal.
>> Is the output of the filter amplitude modulated as a
>> byproduct of the windowing or not?
>
> Clearly not, therefore there is no windowing. Windowing
> ALWAYS modulates the amplitude - that is its function.
>
>>
>>>>> I suspect
>>>>> that the filter response is IFFT'd then convolved with
>>>>> the audio on the fly. That would use least processing,
>>>>> and minimize latency in real-time filtering.
>>
>>>> Prove it!
>>
>>> Prove what? That convolution on the fly is quicker, and
>>> has less latency than taking groups of data, performing
>>> an FFT, multiplying, performing an IFFT then moving
>>> on... do I really need to prove that?
>>
>> Again, you are answering a question that you made up,
>> not the one I asked. The topic is how a particular piece
>> of software does FFT filtering. You either know or you
>> are speculating.
>>
>
> My statement, for which you demanded proof, was that
> convolution against a timebased response was quicker than
> and FFT method which demanded that the audio be cut into
> chunks. That was what you demanded I prove. I decline. It
> is obvious.
>
>>>>> Of course the Audition FFT filter comes with the
>>>>> problem that amplitude and phase responses are not
>>>>> related, so you can't get back to where you started
>>>>> later using minimum phase networks.
>>
>>>> Since Audition also has minimum phase filters readily
>>>> available, its all about giving the user choices.
>>
>>> Sure. I was just making the point that some may not have
>>> grasped about an important aspect of the FFT filter.
>>
>> Only in your dreams.
>>
> You think that everybody in the world understands that
> FFT-based filters do not exhibit a minimum phase
> relationship to their time-based response? Did you
> over-indulge this Christmas?

I see zero relationship between my comments and the recent responses.

For the record, I'm almost a complete teatotaller but one who can drink many
people under the table at will. Weird body chemistry, I guess.

I did drink a long neck bottle of Colorado microbrew around 4 pm on
Christmas day. That's it.