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23rd September 2013, 06:39 PM  #21  
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23rd September 2013, 07:16 PM  #22 
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I am posting an answer here to some question from over at "Uniform directivity" because this discussion does not belong there.
First thing that readers need to understand is that the time/frequency tradeoff, sometimes called the Heisenberg Uncertainty Principle (HUP) is NOT a characteristic of nature, but of the Fourier Transform. In quantum mechanics there is a fundamental connection between a wave function in time and space and this connection is the Fourier integral/transform. Hence, for particles the HUP is fundamental in nature. But it is not so for sampled signals. They do not have to obey the Fourier limitations unless Fourier transforms are actually used. There are many other ways. None of them are as fast (or as simple) as the FFT and that is why it reigns in "typical" applications. But in virtually all advanced applications it is never used for all the reasons that we see here. The FFT is what is called a Nonparametric Spectral Estimator  nonparametric since its frequencies are fixed by the algorithm and all that is found are the values (complex) at those frequencies. There is a whole world of other techniques called Parametric Spectral Estimators which are entirely different that the FFT in every respect, the most important being they do not have a HUP or any form of limitations based on data length or widows etc. These techniques dominate in geological sounding and most importantly (and best known to me) underwater sonar. In those highly advanced areas the FFT is simply never used because it is a very poor technique. Parametric techniques fall into a class of study called "Statistical Signal Processing", on which there are dozens and dozens of text books. In this area of study one must abandon concepts like HUP, and low frequency limits, windows, and any form of "certainty" because everything becomes "estimates" and "uncertainty"  data points become Degreesoffreedom, etc. But please understand that these "estimates" are virtually always better than anything the FFT can produce (the two techniques converge for large data sets and low SNR). So while they are called 'estimates" they are often the best estimates that can be obtained. One can "estimate" a signals frequency, strength and decay based on data that only lasts a very short time, certainly well before the signal has completely decayed. This means that the HUP does not apply. Recently there has been a rash of discussion of how our hearing violates the HUP. To many this may seem surprising but to me it was "natural"  there is no low frequency limit to what our brains can "guess" at. And it turns out that we become pretty good at these guesses  far exceeding the HUP limit of the FFT. So this whole idea that the "resolution" of a measurement is fundamentally limited by the "window" of data is absurd  it doesn't exist. It's there only because people are thinking of FFTs and not the signals themselves. If I wanted to I could write an algorithm that would yield an "estimate" of the frequency response for any given window length all the way to DC. The algorithm would also tell me how "certain" I could be at every frequency in that "estimate". Some frequencies would be better than others and some, like DC, would be pretty bad if the data length was small. But by increasing the data length I could increase my "confidence" up to the point where the noise in the signal was reached. This is exactly the same limit as the FFT, but for short signals the "confidence" in the result is far better for the Parametric approach than the nonPara approach. Why do I use the FFT?, because for what we do it works fine. There is no need to go to the more elaborate techniques because windowing and zero padding is all well understood and works just fine. See Liberty Software (Laud, I believe) if you want to see what can be done with these other techniques. There simply is no need to worry about using windowed FFT data as only an incompetent user would get fooled by its errors. In my polar map program I do use some parametric techniques for the angular data, but not for the frequency data  because there is no need to!! I would if this were an advantage  its not. 
23rd September 2013, 07:21 PM  #23  
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Zero padding does not work for signals that do not decay monotonically  that is well know. But impulses responses are in a class of their own and techniques that do not work elsewhere do work here. 

23rd September 2013, 07:58 PM  #24  
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The rest of your post is undoubtely very interesting (I'd loved to read more), but nothing at all what was discused here so far. Quote:
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Marcel Last edited by mabat; 23rd September 2013 at 08:11 PM. 

23rd September 2013, 09:07 PM  #25 
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Marcel  you are still wrong  but I have lost my patience.

23rd September 2013, 09:41 PM  #26  
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Correct but nobody disagreed so far. It really doesn't matter if you add that "new" information if the IR goes to zero anyway. The question really is "how do you know the IR goes to zero without actually measuring it"? 

23rd September 2013, 10:02 PM  #27  
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But as a side note I stopped using an FFT and I now use an Nth order Prony series (where N is 48000 Hz x .006 sec = 288 Degrees of freedom) which gives me infinite resolution from DC to Nyquist. No window problems, no low frequency limit  Nada. Discussion of resolution of windows is now moot. 

23rd September 2013, 10:21 PM  #28 
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Go at it guys. Great entropy generator.
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John k.... Music and Design NaO dsp Dipole Loudspeakers. 
23rd September 2013, 10:45 PM  #29  
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Despite their ripple, the plots look enormously better than they would if I had used an FFT that only spanned the 5ms window duration, of course, and the centre frequency of the dip is accurately identified. Perhaps that is all you are claiming? Any one of a number of parametric methods would do a better job of recovering the original signal of course, in this case with only 6 poles in the response and noise at the numerical precision of the signal a dozen points would likely do the job (but not zero ones ). responses.png 

23rd September 2013, 11:26 PM  #30  
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The impulse duration in the time domain of a spectral feature is really what is determining how long of a window must be used. For instance in post #2 of this thread, I showed the case of a narrow Q=20, +6dB peak at 2.1kHz that could be resolved quite well with a 5ms window, even though that is only supposed to "see" things with a 200Hz resolution. But the peak's shape and center frequency (2,100 Hz or 10.5 times the resolution) are very well represented in the frequency domain resulting from the 5ms long window. This was using a 50% Hahn window, which as you say should be worse at resolving these features. I did some more testing yesterday and today with peaky responses. If I put a peak with about a 200Hz wide bandwidth at a lower frequency (+6dB, Q=5, 300Hz), it couldn't be seen at all (more or less) with a 5ms window, while the peak at 2.1kHz that I mentioned above looks very nice, even when part of the impulse from it is truncated. All I can say is that the impulse from the 300Hz peak is ringing longer because it is at a lower frequency. If you eliminate too much of the time domain response of a particular feature, it just will not appear in the frequency response. Period. At this point I am not so sure that you can make the assumption that "a 5ms window means you can't see anything narrower than 200Hz anywhere" since this just doesn't seem to be quite true. But I think it is fair to say if your impulse has not died out then you will be eliminating something, and that could just be the low frequency part of the response or it could be a midfrequency peak or dip, it just depends on how that feature come out in the time domain and where your window ends. By the way on page 3 of the Sterophile article "Time Dilation" the author mentions that KEF developed a method by which they attenuated the low frequency response to "help" the impulse die down enough before the end of the window, then applied the inverse filter to the resulting frequency response. I thought that was interesting so I tried it in a few variations to get a feel for what that does. Essentially it doesn't help anything, e.g. it didn't improve the recovery of any peaks that I put in around 200300 Hz (using the 5ms gate). After the whole process was done, there were still errors in the frequency response. These stemmed mostly from the fact that, as was mentioned earlier in one of these threads (by Earl I think) that the frequency response eventually has an "upturn" due to a DC offset in the impulse response resulting from the truncation (if I got what he was saying). This "upturn" caused the frequency response to start deviating "upwards" in SPL from what I knew was the correct FR. I could replace the upturn with a 12dB/oct tail, but at what frequency to do this seemed to be guesswork, although after doing so I could get the SPL accuracy to be within about 23dB down to 20Hz, like they claim. But the response was just a smooth one  you could just generate it using a box model and splice it together with the gated FR in the frequency domain and get about the same thing, so this technique doesn't seem to really offer any new potential in my eyes. 

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