algorithms for location versus for frequency?

Ron N. wrote:
> Peter K. wrote:
> > Ron N. wrote:
> > > To which type of Goertzel algorithm were you refering
> > > and how would you use it for this purpose?
> > http://ptolemy.eecs.berkeley.edu/papers/96/dtmf_ict/www/node3.html#SECTION00030000000000000000
>
> Why would this be any better than a low pass filter followed
> by an average amplitude measurement of some sort?

Actually, I meant band-pass filter, followed by a rectify or square
and the low-pass filter, or perhaps a running average, as one
possible means for the amplitude estimation.

> Thanks.
> -- 
> rhn A.T nicholson d.0.t C-o-M

"Ron N." <rhnlogic@yahoo.com> writes:

> Ron N. wrote:
> >
> > Why would this be any better than a low pass filter followed
> > by an average amplitude measurement of some sort?
> 
> Actually, I meant band-pass filter, followed by a rectify or square
> and the low-pass filter, or perhaps a running average, as one
> possible means for the amplitude estimation.
> 

If you know the frequency, it should be pretty much the same, unless I
misunderstand.

Ciao,

Peter K.

-- 
"And he sees the vision splendid 
of the sunlit plains extended
And at night the wondrous glory of the everlasting stars."

 

Ron N. wrote:
> I suppose all characterization of signals inside some buffer
> of samples is a form of statistical signal processing.
>
> When asking about the frequency of some signal in comp.dsp,
> all sorts of algorithms are mentioned, forms of autocorrelation
> (or squared difference, etc.), or fft/dft usage (windowed, zero
> padded, phase differentiated, etc.).  Also, there seem to be lots
> of textbooks on these forms of analysis in the local libraries.
> Very little mention of statistical techniques from old radar/sonar
> textbooks.

If you are really interested in this sort of stuff, try van Trees'
"detection Estimation and Modulation Theory", vols 1-4.

> However when asking about how to find the location of a signal
> (start/stop/center position), I often get pointers to some signal
> processing textbooks (of which I haven't found any in the local
> library yet).
>
> Why this difference?  Is location analysis less tractable, less
> interesting, or less explainable in comp.dsp algorithms, than
> is frequency?

No. But the methods to be used vary significantly from
situation to situation. In a radar scenario, you have a
fairly "easy" propagation medium, the atmosphere,
between the tranceiver and the target. In air, the speed
of light varies in the 6th or more significant digit. You
usually have a large bandwidth, and you are free to design
the emitted signals.

At the other extreme is exploration seismics. You might have
very little knowledge about the internal structure of the earth,
let alone the detailed propagation velocities in the different
parts and formations. In marine seismic the problem is to
get a sound source that is powerful enough and still possible
to handle, while giving fairly consistent signals between shots.
Forget about any designing of the source signal; some times
one is glad one gets a measurement at all.

What method might work, depends entirely on the context
of the measurement and what you try to achieve by that
measurement.

> For instance, what means could be used to measure the precise
> duration (within rise time uncertainty) of a DTMF tone, Morse
> code dit, or saxaphone note from a buffer of sound samples?

For the first two cases, a naive attempt would be to design
some filter that is centered around the transmitted frequencies
of the various tones, and use some sort of energy detector
at the output. This is possible because the signal sources
are technical contraptions that are designed to emit signals
that other technical contraptions will analyze. The specifications
of the emitted signals are known.

The latter case is a completely different problem. Does the
signal contain sound only from the saxophone, or are other
instruments involved? If there is only one instrument available,
do you need to determine if it is a saxophone or, say, a trumpet
or a clarinet? Is the musician technically proficient, or does he
somehow "mess up" the tone he plays?

These types of questions will come up at one time or another,
We, as human beings, easily can hear the difference between a
saxophone and something else, even when playing with a large
orchestra. I don't know of anybody who have managed to do
anything similar by means of computer analysis.

The reason is that whatever distiguishes the sound of a saxophone
from that of a trumpet or violin, are *qualitative* features, while the

computer only can deal with *quantitative* features.  

Rune

Rune Allnor wrote:
> If you are really interested in this sort of stuff, try van Trees'
> "detection Estimation and Modulation Theory", vols 1-4.

This is exactly the type of thing I've noted before, mentioning
books instead of algorithms.  In comp.dsp, the ratio of discussing
algorithms versus books is far higher for questions about frequency
measurement. Why this difference?  Is frequency measurement
somehow easier to explain?

> > For instance, what means could be used to measure the precise
> > duration (within rise time uncertainty) of a DTMF tone, Morse
> > code dit, or saxaphone note from a buffer of sound samples?
>
> For the first two cases, a naive attempt would be to design
> some filter that is centered around the transmitted frequencies
> of the various tones, and use some sort of energy detector
> at the output.

This solution seems to produce far less resolution and more
error than frequency measurements of the same signals by
methods which have been extensively discussed in the group.
What do the non-naive attempts look like?

> The latter case is a completely different problem. Does the
> signal contain sound only from the saxophone, or are other
> instruments involved? If there is only one instrument available,
> do you need to determine if it is a saxophone or, say, a trumpet
> or a clarinet?

That is a different question.  My question is more about if one did
already have some means (say a priori information) to detect that
there was some saxophone note present, sufficient to measure the
note's frequency content in the signal, how would the methods
for an accurate measurement of the notes duration differ from
those for the measurement of its frequency.


Thanks.
-- 
rhn A.T nicholson d.0.t C-o-M

Ron N. wrote:
> Rune Allnor wrote:
> 
>>If you are really interested in this sort of stuff, try van Trees'
>>"detection Estimation and Modulation Theory", vols 1-4.
> 
> 
> This is exactly the type of thing I've noted before, mentioning
> books instead of algorithms.  In comp.dsp, the ratio of discussing
> algorithms versus books is far higher for questions about frequency
> measurement. Why this difference?  Is frequency measurement
> somehow easier to explain?
> 
> 
>>>For instance, what means could be used to measure the precise
>>>duration (within rise time uncertainty) of a DTMF tone, Morse
>>>code dit, or saxaphone note from a buffer of sound samples?
>>
>>For the first two cases, a naive attempt would be to design
>>some filter that is centered around the transmitted frequencies
>>of the various tones, and use some sort of energy detector
>>at the output.
> 
> 
> This solution seems to produce far less resolution and more
> error than frequency measurements of the same signals by
> methods which have been extensively discussed in the group.
> What do the non-naive attempts look like?
> 
> 
>>The latter case is a completely different problem. Does the
>>signal contain sound only from the saxophone, or are other
>>instruments involved? If there is only one instrument available,
>>do you need to determine if it is a saxophone or, say, a trumpet
>>or a clarinet?
> 
> 
> That is a different question.  My question is more about if one did
> already have some means (say a priori information) to detect that
> there was some saxophone note present, sufficient to measure the
> note's frequency content in the signal, how would the methods
> for an accurate measurement of the notes duration differ from
> those for the measurement of its frequency.

It seems to me that measurements of time are best made in the time 
domain. If the only available information is frequency, I would begin 
with an IFFT.

Jerry
-- 
Engineering is the art of making what you want from things you can get.
�����������������������������������������������������������������������

Jerry Avins wrote:
> Ron N. wrote:
> > Rune Allnor wrote:
> >>If you are really interested in this sort of stuff, try van Trees'
> >>"detection Estimation and Modulation Theory", vols 1-4.
> >
> > This is exactly the type of thing I've noted before, mentioning
> > books instead of algorithms.  In comp.dsp, the ratio of discussing
> > algorithms versus books is far higher for questions about frequency
> > measurement. Why this difference?  Is frequency measurement
> > somehow easier to explain?
> >
> >>>For instance, what means could be used to measure the precise
> >>>duration (within rise time uncertainty) of a DTMF tone, Morse
> >>>code dit, or saxaphone note from a buffer of sound samples?
> >>
> >>For the first two cases, a naive attempt would be to design
> >>some filter that is centered around the transmitted frequencies
> >>of the various tones, and use some sort of energy detector
> >>at the output.
> >
> > This solution seems to produce far less resolution and more
> > error than frequency measurements of the same signals by
> > methods which have been extensively discussed in the group.
> > What do the non-naive attempts look like?
> >
> >>The latter case is a completely different problem. Does the
> >>signal contain sound only from the saxophone, or are other
> >>instruments involved? If there is only one instrument available,
> >>do you need to determine if it is a saxophone or, say, a trumpet
> >>or a clarinet?
> >
> > That is a different question.  My question is more about if one did
> > already have some means (say a priori information) to detect that
> > there was some saxophone note present, sufficient to measure the
> > note's frequency content in the signal, how would the methods
> > for an accurate measurement of the notes duration differ from
> > those for the measurement of its frequency.
>
> It seems to me that measurements of time are best made in the time
> domain. If the only available information is frequency, I would begin
> with an IFFT.

OK.  Say we have the information in the time domain.  What does
one do with this information to measure start/stop times of
some tone?  Analyzing a fixed amplitude signal in zero noise
seems easy.

But what to do if there is static, noise, a fading channel,
and interfering CW signals a few dozens or hundreds of Hz
above and below the tone(s) of interest.  If I use a spectrum
analyzer, I can clearly see my signal separate from all
the interference, and perhaps measure the frequency to 1 Hz
or better.  How do I also measure the length of each dit or
DTMF burst to the best possible accuracy?  I hit the middle
E# key on my piano and can measure the frequency to about
2 cents accuracy.  How long did I hold down the key in mS?
(assuming I'm not using the damper pedal, etc.)

What algorithms are best for these types of measurements?


IMHO. YMMV.
-- 
rhn A.T nicholson d.0.t C-o-M

Ron N. wrote:

   ...

> But what to do if there is static, noise, a fading channel,
> and interfering CW signals a few dozens or hundreds of Hz
> above and below the tone(s) of interest.

The world is an imperfect place.

>                                     If I use a spectrum
> analyzer, I can clearly see my signal separate from all
> the interference, and perhaps measure the frequency to 1 Hz
> or better.  

The spectrogram lets you discover the note and isolate it. A filter 
tuned to it will let you time it. You already rejected the idea, but I 
think it has worth.

> How do I also measure the length of each dit or
> DTMF burst to the best possible accuracy?  I hit the middle
> E# key on my piano and can measure the frequency to about
> 2 cents accuracy.  How long did I hold down the key in mS?
> (assuming I'm not using the damper pedal, etc.)

With a sharp filter tuned to E#.

> What algorithms are best for these types of measurements?

I don't know about best, but time measurements are good. You might even 
count zero crossings. That will establish the duration within a cycle.

The trouble with sharp filters is that the sharper they are, the more 
sluggish their response. Like an FFT, the more accurately you pin down 
the frequency, the less you know about timing. (No surprise.) If noise 
forces you to too narrow a filter in order to isolate the signal of 
interest, the game is up. If you can tolerate a bandwidth of B, then you 
can measure time to the order of 1/B. The "no free lunch" principle is 
at work.

Jerry
-- 
Engineering is the art of making what you want from things you can get.
�����������������������������������������������������������������������

Jerry Avins wrote:
> Ron N. wrote:
> > But what to do if there is static, noise, a fading channel,
> > and interfering CW signals a few dozens or hundreds of Hz
> > above and below the tone(s) of interest.
...
> >                                     If I use a spectrum
> > analyzer, I can clearly see my signal separate from all
> > the interference, and perhaps measure the frequency to 1 Hz
> > or better.
>
> The spectrogram lets you discover the note and isolate it. A filter
> tuned to it will let you time it. You already rejected the idea, but I
> think it has worth.

A filter does not produce time information.  It produces yet another
waveform with somewhat less interference.  So even if I use a filter
(which I am) I am still left with the original problem.  How does one
extract the signal envelope duration with accuracy?

Is the digital equivalent of a rectifier, low-pass and comparator
the best one can do compared to the theoretical bounds?  If so,
how might one optimize the low-pass filter and comparator threshold?

> > How do I also measure the length of each dit or
> > DTMF burst to the best possible accuracy?  I hit the middle
> > E# key on my piano and can measure the frequency to about
> > 2 cents accuracy.  How long did I hold down the key in mS?
> > (assuming I'm not using the damper pedal, etc.)
>
> With a sharp filter tuned to E#.
>
> > What algorithms are best for these types of measurements?
>
> I don't know about best, but time measurements are good. You might
> even
> count zero crossings. That will establish the duration within a cycle.

I assume you meant to say measure zero crossings, not just to
count them.  But I suppose one could count the zero crossings,
and assume that when the number becomes very different from two
per period that the signal has disappeared, and any zero crossings
remaining are likely from noise or interference.  This method
seems very different from trying to estimate the amplitude
and/or threshold of a filtered waveform.  I'll give it a try.

> The trouble with sharp filters is that the sharper they are, the more
> sluggish their response. Like an FFT, the more accurately you pin down
> the frequency, the less you know about timing. (No surprise.) If noise
> forces you to too narrow a filter in order to isolate the signal of
> interest, the game is up. If you can tolerate a bandwidth of B, then you
> can measure time to the order of 1/B. The "no free lunch" principle is
> at work.

Agreed.  The question is what practical (or impractical) methods get
closest to the theoretical bounds of 1/B.  There already seems to
have been a lot of discussion here on how to measure frequency
accurately given a fixed time window.

Thanks.
-- 
rhn A.T nicholson d.0.t C-o-M

> A filter does not produce time information.  It produces yet another
> waveform with somewhat less interference.  So even if I use a filter
> (which I am) I am still left with the original problem.  How does one
> extract the signal envelope duration with accuracy?

Like it has already been suggested to you, I think a sliding Goertzel would 
be a good way to start. Sure a sliding window will always have a low pass 
response, but the rise and fall time of this type of algorithm remains 
constant, resulting in a fairly accurate measure of your "signal envelope" 
duration. But without knowing the exact dynamics of your measurement ie. 
sample rate, bit resolution, S/N ratio, BW of interest etc.. it is 
impossible to tell how accurate it will be.

Thomas 

Ron N. wrote:
> Jerry Avins wrote:

   ...

>>I don't know about best, but time measurements are good. You might even
>>count zero crossings. That will establish the duration within a cycle.
> 
> 
> I assume you meant to say measure zero crossings, not just to
> count them.  But I suppose one could count the zero crossings,
> and assume that when the number becomes very different from two
> per period that the signal has disappeared, and any zero crossings
> remaining are likely from noise or interference.  This method
> seems very different from trying to estimate the amplitude
> and/or threshold of a filtered waveform.  I'll give it a try.

No, I meant count. After the signal is filtered well enough so that an 
octave above it is well attenuated and there's not much noise below it, 
there'll be only one zero crossing per cycle. The signal being buried in 
noise means it's hard to know that it's there. If you can't tell with 
reasonable certainty whether it's there or not, deciding when it starts 
and stops is a fool's errand.

>>The trouble with sharp filters is that the sharper they are, the more
>>sluggish their response. Like an FFT, the more accurately you pin down
>>the frequency, the less you know about timing. (No surprise.) If noise
>>forces you to too narrow a filter in order to isolate the signal of
>>interest, the game is up. If you can tolerate a bandwidth of B, then you
>>can measure time to the order of 1/B. The "no free lunch" principle is
>>at work.
> 
> 
> Agreed.  The question is what practical (or impractical) methods get
> closest to the theoretical bounds of 1/B.  There already seems to
> have been a lot of discussion here on how to measure frequency
> accurately given a fixed time window.

That depends on B. A bandpass filter of 100 Hz allows discrimination of 
about 10 ms, and is pretty narrow band even of middle C.

Jerry
-- 
Engineering is the art of making what you want from things you can get.
�����������������������������������������������������������������������