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On 4/29/2010 9:33 AM, Invisible wrote:
>> I thought it looked as one would expect it to, personally.
>
> I'm rather surprised that it's a real effect. I thought it only happens
> in Hollywood action films. But, apparently, it's real. Even if it does
> look hard to believe...
What always surprising to me is how a real detonation is so
underwhelming compared to a Hollywood detonation. No fireball, just a
loud report, the shockwave and a dust cloud.
--
~Mike
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Mike Raiford wrote:
> What always surprising to me is how a real detonation is so
> underwhelming compared to a Hollywood detonation. No fireball, just a
> loud report, the shockwave and a dust cloud.
I think that depends on whether it's a controlled explosion or not. If
you're intentionally blowing up a building, you don't *want* much more than
that. If the propane tank catches on fire, yeah, it's probably gonna be louder.
--
Darren New, San Diego CA, USA (PST)
Linux: Now bringing the quality and usability of
open source desktop apps to your personal electronics.
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Darren New <dne### [at] san rrcom> wrote:
> Mike Raiford wrote:
> > What always surprising to me is how a real detonation is so
> > underwhelming compared to a Hollywood detonation. No fireball, just a
> > loud report, the shockwave and a dust cloud.
>
> I think that depends on whether it's a controlled explosion or not. If
> you're intentionally blowing up a building, you don't *want* much more than
> that. If the propane tank catches on fire, yeah, it's probably gonna be louder.
Has anyone seen The Kingdom? There's a large carbomb towards the end that was
actually done for real with the explosives they said were used in the script...
looked terrifying. And not at all typical hollywood :)
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> Sure, it's not expensive for suitably large definition of "expensive". ;-)
Usually "expensive" means expensive for the apparent/usual value. eg a new
car for 5000 pounds is not expensive, but a phone for 1000 pounds is
expensive.
Not for a specialist camera.
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>> Sure, it's not expensive for suitably large definition of "expensive".
>> ;-)
>
> Usually "expensive" means expensive for the apparent/usual value. eg a
> new car for 5000 pounds is not expensive, but a phone for 1000 pounds is
> expensive.
>
>
> Not for a specialist camera.
What I'm saying is that it's far more expensive than anything *I* could
ever afford.
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Mike Raiford wrote:
> It's amazing when you look at how your auditory system works. Your brain
> essentially gets the Fourier transform of what you're listening to.
Be careful with that...
Audio systems generally represent sound in the time domain. The Fourier
transform moves data from the time domain to the frequency domain. But
humans don't hear sound in the frequency domain; they hear it in a
*combination* of both domains - i.e., as a spectrum that changes over time.
You can take a 5-minute song and take the Fourier transform of it, and
that will only tell you what combination of [fifty zillion] sine waves
you need to add together to get back the same waveform. That's not how a
human would perceive it; they hear notes and beats and things - sounds
*changing* over time, but one static combination of frequencies.
It turns out this is one of the most tricky things in DSP. Working with
signals that have a meaning in *both* domains...
(You might think, for example, that you could just snip your 5-minute
song into, say, 50 ms chunks and take the Fourier transform of each
chunk. Alas, snipping it up introduces phantom frequencies that aren't
really there.)
I've spent a significant amount of time trying to come up with some
mathematics for analysing sound the way that the human auditry system
does... So far, nothing works.
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> (You might think, for example, that you could just snip your 5-minute song
> into, say, 50 ms chunks and take the Fourier transform of each chunk.
> Alas, snipping it up introduces phantom frequencies that aren't really
> there.)
Use something other than a rectangular window function then, that helps with
the phantom frequencies.
> I've spent a significant amount of time trying to come up with some
> mathematics for analysing sound the way that the human auditry system
> does... So far, nothing works.
If you don't need it in realtime then just run a band-pass filter over the
whole song at varying frequencies, you can then read off the amplitude at
any frequency at any time during the song.
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scott wrote:
>> (You might think, for example, that you could just snip your 5-minute
>> song into, say, 50 ms chunks and take the Fourier transform of each
>> chunk. Alas, snipping it up introduces phantom frequencies that aren't
>> really there.)
>
> Use something other than a rectangular window function then, that helps
> with the phantom frequencies.
The [discrete] Fourier transform reduces a signal to a finite number of
frequencies by assuming that the signal is periodic - i.e., the samples
you've fed it repeat forever. If the endpoints don't match up, that
introduces a discontinuity, which causes phantom frequencies to appear.
(They represent the discontinuity.)
You can reduce this by using another window function - but then,
strictly speaking, you're multiplying your signal by a window, which
convolves its spectrum with the spectrum of the window. In other words,
by windowing the signal you're blurring its spectrum.
>> I've spent a significant amount of time trying to come up with some
>> mathematics for analysing sound the way that the human auditry system
>> does... So far, nothing works.
>
> If you don't need it in realtime then just run a band-pass filter over
> the whole song at varying frequencies, you can then read off the
> amplitude at any frequency at any time during the song.
This, of course, is effectively what the human ear does. (As in, the
physical organ, not the elaborate post-processing that undoubtably
happens when the data reaches the brain.) Each hair in the cochlea is
essentially a crude band-pass filter. (I don't imagine they actually
have a great frequency response; much like the human eye isn't actually
a very good camera.)
So far, everything I've ever learned about DSP seems to hinge on one
single equation:
sin X + sin Y = 2 cos (X-Y)/2 sin (X+Y)/2
This says many things. But most obviously, it says that the sum of any
two waves is also the product of two *other* words, and vice versa. So
the question is, at what frequency does a wave become "sound" rather
than "variation in sound"?
The *other* question, of course, is "how many frequency bands do I need?"
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On 4/30/2010 7:02 AM, Invisible wrote:
> Mike Raiford wrote:
>
>> It's amazing when you look at how your auditory system works. Your
>> brain essentially gets the Fourier transform of what you're listening to.
>
> Be careful with that...
Yeah, I was simplifying greatly what is happening... If you want to
display a voiceprint or Realtime spectrum, you'll need to move a window
across the signal.
> (You might think, for example, that you could just snip your 5-minute
> song into, say, 50 ms chunks and take the Fourier transform of each
> chunk. Alas, snipping it up introduces phantom frequencies that aren't
> really there.)
>
> I've spent a significant amount of time trying to come up with some
> mathematics for analysing sound the way that the human auditry system
> does... So far, nothing works.
Yeah, someone said it before, but there are windowing functions for
that. If you want to get quick and dirty, you can use a rectangular
window, but this will introduce artifacts in the resulting spectrum.
Strangely enough the other day I was experimenting with windowing in a
Fourier transform to analyze a rather complex FSK signal (Trying to get
a grip on how the signal worked ...) The major problem I had with it was
the fact that there was significant smearing between samples, that I
couldn't tell when the frequencies were changing, not only that it made
it exceedingly difficult to determine what the frequencies were. I tried
various windowing functions and window sizes.
I should pick up my little DSP project I was working on and fiddle with
it again.
--
~Mike
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Mike Raiford wrote:
> Strangely enough the other day I was experimenting with windowing in a
> Fourier transform to analyze a rather complex FSK signal (Trying to get
> a grip on how the signal worked ...) The major problem I had with it was
> the fact that there was significant smearing between samples, that I
> couldn't tell when the frequencies were changing, not only that it made
> it exceedingly difficult to determine what the frequencies were. I tried
> various windowing functions and window sizes.
Yeah, Scotts idea of using a bank of bandpass filters is probably the
way to go for that. Split the signal into several dozen signals, each
containing only a narrow band of frequencies. See how much energy each
signal has.
(I have no idea how you measure signal energy...)
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