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Old 24th May 2018, 03:00 PM   #211
gedlee is offline gedlee  United States
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Kees

Thanks for the offer, but I am not sure how one would sue such a model. The basis of comparison for this measurement technique is the old way speakers were measured. Its not as if those methods were inaccurate, just inconvenient. How we would get this data on an identical speaker for comparison is not clear to me either though.

How would you use the model? We will only be gating initially and so room reflections won;t be part of the problem.
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Old 24th May 2018, 03:25 PM   #212
kessito is offline kessito  Netherlands
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We could compare the BEM model data with the room included, to the BEM model data without the room included (simulated anechoic).

I understand that the first steps are with gating (of <3ms because of floor reflection?), but I guess that for the next steps comparison of the anechoic (BEM model) data with the processed data of the BEM model with room included could be useful, especially because there won'tbe any small placement/temperature etc real world error's in the data and so it will show the efffectiveness of the algoritms.

I think that even for the first steps it might be useful to have data where e.g. 10 degree of axis is exactly that and not 10.5 degree.
I personally find it very difficult to have the angles and point of rotation exact during real measurements, I know that normally this isn't such a big deal but maybe for verifying the algorithms it would be better not to have these errors.

I would make a true 3D model, so we could also simulate asymetrical enclosures .

I can also make some real acoustical measurements, but I have a real small (typical Amsterdam ) room, so the refflection free window will be rather small.

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Old 24th May 2018, 04:30 PM   #213
gedlee is offline gedlee  United States
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Quote:
I know that normally this isn't such a big deal but maybe for verifying the algorithms it would be better not to have these errors
Actually it is the exact opposite. How robust the algorithms are to real world errors is the core of success. Without going into too much detail, the inverse problem (finding the source velocity distribution from the pressure) is singular as ka ->0. The noise (errors) in the measurements get magnified by this effect and this runaway situation has to be handled somehow. Without real-world errors in the data, one might not ever even see the real-world problems.
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Old 25th May 2018, 04:58 AM   #214
scott wurcer is offline scott wurcer  United States
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Quote:
Originally Posted by gedlee View Post
The noise (errors) in the measurements get magnified by this effect and this runaway situation has to be handled somehow.
Exactly the same problem with image restoration with inverse filters. Do you think any of the maximum entropy iterative techniques have any use here?
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Old 25th May 2018, 06:24 AM   #215
Dave Zan is offline Dave Zan  Australia
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..The noise (errors) in the measurements get magnified by this effect and this runaway situation has to be handled somehow.
Yes, I replied to Kees on this way back in post #78, so you may have missed it.
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This may not be so easy, one could say that as we "zoom in" then any errors in the data are expanded too.
Or, as mathematicians would say, "the inverse problem is ill conditioned".
I don't know if this applies to exactly your idea but my intuition is that there will be serious problems with data sensitivity to noise, measurement inaccuracy etc.
Do we need source velocity reconstruction?
Not to simulate anechoic chamber response measurements AFAIK.

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...Do you think any of the maximum entropy iterative techniques have any use here?
But the maths sounds like fun
Do you have a specific reference that you recommend?

Best wishes
David

This does raise the question of how accurate our measurements need to be.
An inexpensive stepper motor gearhead has backlash typically within one hundredth of a radian.
I expect that's adequate, anyone have experience?

Last edited by Dave Zan; 25th May 2018 at 06:34 AM.
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Old 25th May 2018, 10:53 AM   #216
scott wurcer is offline scott wurcer  United States
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But the maths sounds like fun
Do you have a specific reference that you recommend?
One of the classics http://bayes.wustl.edu/sfg/why.pdf but there are many. These guys would tax Cray XMP's in the day and yes they used FORTRAN.
I was just curious since optical and acoustic problems use some of the same mathematical tools. 20yr. ago I wrote an FFT in assembly as part of an evening grad school project for the 68040's FPU and a day job customer (GE Medical) asked if they could use it in a CAT scanner.

Here's a totally different approach with pictures of sucking a useful image out of noisy degraded data. ftp://www.adass.org/adass/proceeding...puetterrc.html
This is possibly just a distraction but it was fun stuff when the embarrassment of the first Hubble images was obvious.
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Old 25th May 2018, 03:38 PM   #217
gedlee is offline gedlee  United States
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Scott and David

I don't think that either of you has exactly the right idea. This issue is not what is discussed in #78 and from what I could tell from reading Scott's links it's not the same as image reconstruction.

Theoretically this issue is not a problem, but when it comes time to implement the concepts in code, they will blow up. It took me almost ten years to discover the whys behind this and to develop a work around. Hence, at this point this is one area that I am not willing to discuss in detail. It will have to remain proprietary for now.

My experience with these techniques is that once you get past the singularity issue above, they are extremely robust to errors and noise, but you have to get past the singularity issue. Errors and noise in the measurements just don't fit the model so they get ignored. The singularity issue is unlike image processing, but the rest is quite a bit like image processing.

David - we do not have to do source reconstruction, but the singularity issue is present even if you don't.
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Old 26th May 2018, 01:43 AM   #218
Dave Zan is offline Dave Zan  Australia
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Originally Posted by scott wurcer View Post
One of the classics...
Thanks, that whole site is fun even if it's not exactly what we need.

Quote:
Originally Posted by gedlee View Post
Theoretically this issue is not a problem, but when it comes time to implement the concepts in code, they will blow up. It took me almost ten years to discover the whys behind this and to develop a work around...
I had concerns about data sensitivity and singularity at r=0 but not well defined, hence my question and "AFAIK".
I will think about it some more, I wonder how it plays out for the Klippel NFS.

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David
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Old 26th May 2018, 03:41 PM   #219
gedlee is offline gedlee  United States
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Its not the r -> 0 problem as much as it is the k -> 0 problem. r never goes to zero. If you look at my equation 3 and 6 you will see a ratio of Hankel functions. These grow very large as k -> 0 at ever steeper rates as m grows. If this is not handled in the code the calcs will eventually overflow.

I suspect that Klippel uses a matrix approach and singular value decomposition which deals with issues like this quite readily. My approach, as shown in my paper, is not matrix based and uses integrals to find the coefficients. Those integrals blow up for m>0 as ka -> 0 at an ever increasing rate with m.
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Last edited by gedlee; 26th May 2018 at 03:45 PM.
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Old 29th May 2018, 12:36 AM   #220
Dave Zan is offline Dave Zan  Australia
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Quote:
Originally Posted by gedlee View Post
...If you look at my equation 3 and 6...
Equations 3 and 6 where?

Quote:
My approach, as shown in my paper...
Which paper? The Mathcad PDF doesn't have the equations numbered or seem to fit the description (?).

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David
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