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11th August 2010, 11:03 PM  #7121  
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Join Date: Aug 2004
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Quote:
It's really a chicken  egg argument.
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John k.... Music and Design NaO dsp Dipole Loudspeakers. Last edited by john k...; 11th August 2010 at 11:13 PM. 

11th August 2010, 11:39 PM  #7122  
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John, that was not the context of my objection to the terminology:
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12th August 2010, 12:16 AM  #7123  
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John k.... Music and Design NaO dsp Dipole Loudspeakers. 

12th August 2010, 01:20 AM  #7124  
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Location: Northland, New Zealand

Quote:
Isn't that like saying "the engine speed and throttle position gives all the information needed to determine fuel injection quantity on an engine"? The analogy between speakers and engines may be useful; engines exhibit some dependence on transient inputs. For example, this is why acceleration enrichment is still required on fuel injected engines. I would think that any system that exhibits energy storage and release cannot be completely understood from its "steady state" behaviour. 

12th August 2010, 05:43 AM  #7125  
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Join Date: Jan 2005
Location: Austria, at a beautiful place right in the heart of the Alps.

John and John
my guesstiimation of Elias line of arguments is that he is fully aware of what you outline  though he is emphasizing on the point that you need quite some "reading the tea leaves" experience to predict whats going on from IR and FR only. As was made pretty clear  both IR and FR are just two sides of the same coin  *but* you always have to (in the sense of : easier you do so) look at both sides to get the rather complete pix and even then its way less (intuitively) clear how to "read" the IR/FR plots compared to a timefrequency plot. So the difference in comparing "wavelet versus IR/FR" is more in the user friendliness and accessibility as well as in the scalability of wavelet versus IR/FR. Sure the system is determined also by FR/IR but it simply is a PITA to distinguish reflections from normal resonance from looking at FR/IR only. I guess this is why CMP behaviour hasn't made it into consciousness of audio geeks until now. The second point in Elias line of arguments  as I see it  is valid as well. The more "steadyness" we look for in measurement (making it a looooong process) the better accuracy  possibly not so much because we could not gain the exact same information by a very short pulse measurement  but simply from mere practical circumstances (background noise compromising accuracy). Quote:
I'm though still awaiting an outspoken answer if a CMP system is in any case determined by IR/FR just the same. To me, a discontinuity in temporal behaviour  as happening in CMP systems  questions that (most obviously seen in the  theoretically  100% correctability of deep nulls)  but as said  thats better be answered by the math magicians. Michael
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Audio and Loudspeaker Design Guidelines Last edited by mige0; 12th August 2010 at 05:55 AM. 

12th August 2010, 07:43 AM  #7126  
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Firstly, let me apologise to Elias as my posts probably come across as being directed personally at him. That is not my intent, what I am against is the common treatment of time and frequency domains as being somehow unconnected, which can creep into our thinking in many subtle and not so subtle ways and lead to erroneous conclusions. I'll illustrate using the post on sweeps, as it simultaneously contains much well considered truth and yet much fallacy.
Quote:
 To measure a frequency response we should really use a frequency, and the more like a frequency (sine wave) the test signal is the better the result would be  A sweep is a frequency signal and the shorter you make it the less like a frequency it becomes and so the less suited it is to measuring frequency response. All signals are time signals, a sweep is no less a time signal than an impulse or a step. It is easy to start thinking about a sweep as if it lived in the frequency domain, we talk about its start and stop frequencies after all, but that is already the start of misunderstanding it. All time signals have spectra in the frequency domain, and those spectra are nearly always far richer than we imagine. A sweep that starts at 20Hz and ends at 200Hz has a spectrum that extends from DC to Nyquist, reducing the content outside the notional range of the sweep requires considerable care in tapering the onset and decay of the signal, and even then the levels outside the range we may be most interested in are only reduced, they are not zeroed. To address the DC comment, a single half cycle of a 1kHz sine wave has DC content. So does a single half cycle of a 1MHz wave or a 1GHz wave. An impulse has DC content! The slightest imbalance in a time signal yields DC content, constructing a sweep without DC is quite difficult (and of course unnecessary). A system's frequency response is just a way of describing its behaviour (all its behaviour, not just "steady state"), that description is valid in the time and frequency domains. To determine it all we need to do is pass a signal through the system and divide the spectrum of the output by the spectrum of the input (that division is the reason the spectrum of the signal used must not be zero anywhere). The quality of the result is determined by how much energy the test signal contains across the whole spectrum within the linear range of a system, a sweep that is twice as loud improves the accuracy of the derived frequency response by the same amount as a sweep that lasts twice as long. That frequency response can then be used to generate the time or frequency responses of the system to any stimulus by simply multiplying it by the spectrum of the stimulus. To see that as a time response, just inverse FT the product. None of this is an argument against the utlility of different ways of presenting the information contained in a signal, whether they look purely at time, frequency or a combination of the two, but it is important not to treat frequency as being somehow independent of time or frequency responses as not containing full information about time domain behaviour. 

12th August 2010, 09:50 AM  #7127  
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Hello,
Is there any change to access these open baffle impulse responses? (I didn't read the whole thread ) It would be interesting to compare them to the one posted measured in the box.  Elias Quote:
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12th August 2010, 12:12 PM  #7128  
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Hi JohnPM,
In order to get over this issue you may like to review the properties of the Fourier transform, but you must know it already which makes it strange why someone would argue over such a basic fundamental definition. FT transforms time domain to frequency domain where time information is lost, thus frequency domain is steady state domain. FT does not care what you transform, anything in time domain signal will end up into steady state frequency domain signal even your time domain signal is not steady state. Frequency response is a frequency domain concept, thus frequency response is a steady state response. This is what frequency response IS. One can use it freely to suit his purposes. It's not clear to me if you agree or not about your quote below, but if you don't mind would you explain how to measure the frequency response? Yes, I emphasize to measure to differentiate it from to calculate, that is to determine frequency response without FT. Anyway, I gave the answer earlier in my post which is also the definition of the frequency response, but it does not exclude you to give a different answer.  Elias Quote:
Last edited by Elias; 12th August 2010 at 12:16 PM. 

12th August 2010, 01:47 PM  #7129  
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Quote:
No information is lost when FT is performed None at all. To understand why, it may help to consider what the FT is, but from a different perspective. There are many ways to represent a time signal. The most familiar are some equation in which time is a variable, or for our purposes a sequence of the amplitudes of the signal sampled at uniform time intervals. There are circumstances, however, where it would be useful to be able to represent that time signal in other ways that are more amenable to certain kinds of processing. One way to do this is to take some other set of time functions and see if we can find a way to create the original time signal through a combination of those functions. The functions are referred to in mathematical jargon as basis functions. In the case of FT (and here we are considering the Discrete Fourier Transfrom, since our signal is defined at the discrete time intervals at which we sampled it) we choose sine waves as the time functions. When we calculate the DFT of our signal we are finding the answer to the question "what are the amplitudes and phases of a set of sine waves that when added together will give the original time signal". (Strictly speaking, we calculate the real and imaginary parts of a complex number whose amplitude and phase are the answers we seek). We call that set of sine wave amplitudes and phases the frequency response. After we have done this, we can perform processing that alters the amplitudes and phases of those sine waves rather than altering the samples of the time signal directly, but we are still dealing with the original time signal, just represented in a different way. If we want to get back to the time series we just perform an inverse FT, which simply adds up the sine waves for us to get back to the time signal. In the context of a system and its transfer function, the time signal we are representing is the impulse response. The system frequency response is just a different way of representing the impulse response, it still contains all the information the impulse response contains. Quote:
Fortunately, we can go directly from time signals to the real and imaginary parts of the frequency response amplitudes and phases. To do this we need a signal generator that will produce a sine and a cosine signal. We use our generator to produce two sweep signals, the sweep from the sine wave output goes to the system we want to measure. That sine sweep and the cosine sweep are both fed through time delays that match the time delay through the system we are measuring, in the acoustic case that would typically be the time it takes sound to travel from the speaker to the mic  we need to determine the delay in advance, but that is not difficult to do. The signal captured from the mic goes to two multipliers, in one of them it is multiplied by the sine signal, in the other it is multiplied by the cosine signal. Each multiplier has a low pass filter on its output. The output of the sine wave multiplier (after the low pass filter) is the real part of the frequency response, the output of the cosine multiplier is the imaginary part. Since we are controlling the generator we know the frequency the generator was at for each moment during our sweep, so we know what frequency the real and imaginary signals correspond to. So, in a matter of a few seconds of a sine sweep, we have captured the frequency response of the system. And the FT was left in the toolbox, unwanted. That process is called Time Delay Spectrometry, it is typically used with linear sweeps as that makes life a bit easier in terms of the effects of the low pass filters. It is the measuring principle used in the TEF system. TDS has some advantages (it is quite good at rejecting noise, distortion and room reflections) but also some disadvantages. Log swept sine is overall a better method of transfer function measurement, but of course that uses the dreaded FT I hope that helps. 

12th August 2010, 03:26 PM  #7130  
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Hi,
I should have been more clear in my previous statement, but actually there IS information loss in the process of transforming the time domain to the frequency domain. What is lost is the information if the input signal was at the steady state or not before the FT was performed. There is no means in FT to express that information. FT allways outputs steady state freq response regardless of input, but when calculating IFT no information is available if the time domain signal should be at steady state or not. Usually one assumes it is, but generally it is unknown. If one inputs arbitrary signal into the system and calculates FT at the output, the result will vary until the steady state is reached. In steady state input and output signals must also be steady state signals. This will take us back into measuring the frequency response and the importance of reaching the steady state before a freguency measurement achievement is declared. And of course since we are measuring at the steady state the frequency response must be a steady state measurement. LOL did I say it again Never mind Quote:
 Elias 

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