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similar sounds need to be identified.... ideas?

Started by NightHawk October 1, 2005
There are 2 very similar sounds, and I need a method to automatically
identify if its of type 1 or type 2. Sounds of type 1 have a manufacturing
defect, while sounds of type 2 have no defects.

The problem is the sounds are very similar, and so far, I have tried the
Fourier Transform to try and find something that makes each sound unique(
one or more specific frecuencys present in items with or without defect),
but so far no luck.... 

What other methods can I try to identify something that makes helps me
identify the type of sound?. 
Please help me, as Im out of Ideas!!!

Must be a somewhat speedy method(1 or 2 seconds at most for real time
audio sampling, processing, and test result).
		
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NightHawk wrote:
> There are 2 very similar sounds, and I need a method to automatically > identify if its of type 1 or type 2. Sounds of type 1 have a manufacturing > defect, while sounds of type 2 have no defects. > > The problem is the sounds are very similar, and so far, I have tried the > Fourier Transform to try and find something that makes each sound unique( > one or more specific frecuencys present in items with or without defect), > but so far no luck.... > > What other methods can I try to identify something that makes helps me > identify the type of sound?. > Please help me, as Im out of Ideas!!! > > Must be a somewhat speedy method(1 or 2 seconds at most for real time > audio sampling, processing, and test result). > > This message was sent using the Comp.DSP web interface on > www.DSPRelated.com
What are the sounds?
>NightHawk wrote: >> There are 2 very similar sounds, and I need a method to automatically >> identify if its of type 1 or type 2. Sounds of type 1 have a
manufacturing
>> defect, while sounds of type 2 have no defects. >> >> The problem is the sounds are very similar, and so far, I have tried
the
>> Fourier Transform to try and find something that makes each sound
unique(
>> one or more specific frecuencys present in items with or without
defect),
>> but so far no luck.... >> >> What other methods can I try to identify something that makes helps me >> identify the type of sound?. >> Please help me, as Im out of Ideas!!! >> >> Must be a somewhat speedy method(1 or 2 seconds at most for real time >> audio sampling, processing, and test result). >> >> This message was sent using the Comp.DSP web interface on >> www.DSPRelated.com > >What are the sounds? > >
The sounds are the result of hitting a ceramic "cone" with a hammer. When the ceramic cone was not well cooked, its sound is slightly diferrent because of the internal defects. This message was sent using the Comp.DSP web interface on www.DSPRelated.com
"NightHawk" <nightstalker_099@yahoo.com> wrote in message 
news:DMSdnR9Bibngx6LeRVn-pg@giganews.com...
> >NightHawk wrote: >>> There are 2 very similar sounds, and I need a method to automatically >>> identify if its of type 1 or type 2. Sounds of type 1 have a > manufacturing >>> defect, while sounds of type 2 have no defects. >>> >>> The problem is the sounds are very similar, and so far, I have tried > the >>> Fourier Transform to try and find something that makes each sound > unique( >>> one or more specific frecuencys present in items with or without > defect), >>> but so far no luck.... >>> >>> What other methods can I try to identify something that makes helps me >>> identify the type of sound?. >>> Please help me, as Im out of Ideas!!! >>> >>> Must be a somewhat speedy method(1 or 2 seconds at most for real time >>> audio sampling, processing, and test result). >>> >>> This message was sent using the Comp.DSP web interface on >>> www.DSPRelated.com >> >>What are the sounds? >> >> > The sounds are the result of hitting a ceramic "cone" with a hammer. When > the ceramic cone was not well cooked, its sound is slightly diferrent > because of the internal defects.
Can the human ear easily detect the difference?
Jon Harris wrote:
> "NightHawk" <nightstalker_099@yahoo.com> wrote in message > news:DMSdnR9Bibngx6LeRVn-pg@giganews.com... > >>>NightHawk wrote: >>> >>>>There are 2 very similar sounds, and I need a method to automatically >>>>identify if its of type 1 or type 2. Sounds of type 1 have a >> >>manufacturing >> >>>>defect, while sounds of type 2 have no defects. >>>> >>>>The problem is the sounds are very similar, and so far, I have tried >> >>the >> >>>>Fourier Transform to try and find something that makes each sound >> >>unique( >> >>>>one or more specific frecuencys present in items with or without >> >>defect), >> >>>>but so far no luck.... >>>> >>>>What other methods can I try to identify something that makes helps me >>>>identify the type of sound?. >>>>Please help me, as Im out of Ideas!!! >>>> >>>>Must be a somewhat speedy method(1 or 2 seconds at most for real time >>>>audio sampling, processing, and test result). >>>> >>>>This message was sent using the Comp.DSP web interface on >>>>www.DSPRelated.com >>> >>>What are the sounds? >>> >>> >> >>The sounds are the result of hitting a ceramic "cone" with a hammer. When >>the ceramic cone was not well cooked, its sound is slightly diferrent >>because of the internal defects. > > > Can the human ear easily detect the difference? >
I suggest measuring how long the 'ringing' response takes to decay to, say, ten per cent of its original amplitude, or even better, how long the response takes to pass between two specific amplitudes. The cone with the defects should decay at a higher rate. Regards, John
No, it requires a trained ear to be able to detect the diference, and even
then sometimes you cant tell for certain if a piece is ok or has a defect.
It is my hope that a machine hearing, since it can concentrate on a
particular frecuency or parameter, has an easier time separating the good
pieces from the bad.

		
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If you want, Ill upload some recording of both good and defective pieces,
so you can hear your yourself and get and idea of what the problem is.
Maybe you can suggest something if you hear them for yourself...
		
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NightHawk wrote:
> No, it requires a trained ear to be able to detect the diference, and even > then sometimes you cant tell for certain if a piece is ok or has a defect. > It is my hope that a machine hearing, since it can concentrate on a > particular frecuency or parameter, has an easier time separating the good > pieces from the bad. > > > This message was sent using the Comp.DSP web interface on > www.DSPRelated.com
There are a couple of things that might work. Start with standardized hammer tap or two (different places or hammer material) delivered by a consistent machine and a standard microphone and placement, feeding a detailed FFT as in machine signature analysis. Use the cone as part of an acoustic feedback loop with transmitting and receiving transducers in prescribed sets of locations. Record the gain needed for oscillation and the frequency for each position. Compare a batch of good cones with a batch of bad ones; make a table or train a neural net to separate them. Jerry -- Engineering is the art of making what you want from things you can get. &#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;&#2013266095;
NightHawk wrote:
> There are 2 very similar sounds, and I need a method to automatically > identify if its of type 1 or type 2. Sounds of type 1 have a manufacturing > defect, while sounds of type 2 have no defects. > > The problem is the sounds are very similar, and so far, I have tried the > Fourier Transform to try and find something that makes each sound unique( > one or more specific frecuencys present in items with or without defect), > but so far no luck.... > > What other methods can I try to identify something that makes helps me > identify the type of sound?. > Please help me, as Im out of Ideas!!! > > Must be a somewhat speedy method(1 or 2 seconds at most for real time > audio sampling, processing, and test result).
The solution will require two parts: an appropriate feature extraction (you mention trying FT), and a classifier which operates on the provided features to render a classification (you didn't mention, above, how you used the FT). A little creativity goes a long way toward making the classifier's job easy. Obviously, just about any signal processing tool or trick that your hardware will support is fair game. I'd suggest starting with simpler alternatives since they tend to require less effort to implement and to execute faster. I once read about a speech recognition solution (for spoken digits, 0 to 9) which merely segmented the digitzed sample of the users voice into 24 regions (4 bins in amplitude, 6 in time). A fuzzy logic rule set was applied to the counts of the samples which fell in each region to classify the user's speech. Classifiers also come in a wide variety. Since you are posting this in a DSP forum and have indicated familiarity with FT, I conclude that you are most likely a DSP person, not a statistician or machine learning person. That is fine. Some classifiers are easy to understand and are effective at solving problems, especially when fed good features. Again, I recommend starting with the simplest, such as linear discriminant. -Will Dwinnell http://will.dwinnell.com
I'd go with Jerry's advice to use several audio and/or vibration
sensors placed in various physical locations around the clay pots.

But I would recommend the following processing:

1. Array (vector) processing instead of scalar (1-D) processing: since
you have a vector measurement per time-point (e.g. if you have 5
sensors, then at each sampling time you have a 5-element vector of
measurements). Therefore, if you're really serious about a good
solution, take a look at books about Array Processing.

2. Do not use a regular FFT for spectrum estimation. For example, in
1-D instead of FFT use some AR (auto-regressive) estimator such as Burg
or Covariance. In vector cases there are appropriate extensions.

Notes:
- Audio sensors (microphones) don't have to actually touch the object,
but vibration sensors usually do, so vibration sensors have a
disadvantage.
- The sensor position should be "standardized" and also the "hammer"
(it's velocity, hitting position, and hitting force).

Gidi