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Science

Introduction to Wavelets 16

tang_horse writes "If you're curious about where wavelet theory came from and how it works, but didn't get beyond second semester DQ (or algebra/trig I), there's a lucid review, history, and introduction to wavelet theory on the National Academy of Sciences Beyond Discovery website. It includes some fairly concrete demos of what a wavelet function does to an image."
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Introduction to Wavelets

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  • ....of radioactive information transfer between neutrinos and massless artifacts of a riemannian transform. And I can say that this article is very oversimplified where it is even right. For example, take this quote:

    Throw a stone into a pool. Those are waves. Take an infinitesimally small arc segment from one of those waves and you have a wavelet, basically.

    Totally double-talk that has nothing to do with the soliton basis for Wavelet Theory (WT).

  • by dschuetz ( 10924 ) <davidNO@SPAMdasnet.org> on Friday March 22, 2002 @02:12PM (#3208873)
    From the paper, lines like "On his personal computer, he could separate a wave into its wavelet components, and then reassemble them into the original wave" and "could be implemented using simple digital filtering ideas, in fact, using short digital filters" prompt me to ask: Just how does one do signal processing on a PC?

    I haven't done any programming with sound files since I tried to play a .au file backwards on a NeXT like 13 years ago. Are there really simple libraries that you can use where you say something like "out = lowpass(in.wav, 500)" and get just the bass from a wav file? How, then, do you do funky stuff like "Lt = 0.92Lf + 0.38Rf + j0.92Lb + j0.38Rb" or somesuch, where "j" represents a 90-degree phase shift? (bonus points to anyone who can identify the formula I'm screwing up).

    And how do these libraries work? Is it basically iterating stuff over a huge array of amplitudes? So that to implement the lowpass filter, you actually have to somehow or other scan the array to look for patterns that need to be smoothed out? (actually, it occurs to me that you might be able to do this by averaging adjacent entries together. Okay, then, how do you do a high-pass filter, keeping the local oscillations but dropping the low-frequency ones?)

    Basically, I'm curious about a good primer for DIY sound processing. Not because I want to build my own sound processing stuff, but because I'm really curious how people can do stuff like what Morlet did, especially considering he did it like 20 years ago, with significantly less-powered hardware...

    (and, yes, I searched google for this, but it was a year or two ago...didn't find anything really helpful, except an early framework of an open source library that hadn't progessed much).
    • See http://www.ti.com/cgi-bin/sc/search.cgi - go nuts. We (plug alert - I work at TI) have tons of PC based software to interface with our DSP's for audio applications.
    • by ChadN ( 21033 ) on Friday March 22, 2002 @04:29PM (#3209831)
      And how do these libraries work? Is it basically iterating stuff over a huge array of amplitudes? So that to implement the lowpass filter, you actually have to somehow or other scan the array to look for patterns that need to be smoothed out? (actually, it occurs to me that you might be able to do this by averaging adjacent entries together. Okay, then, how do you do a high-pass filter, keeping the local oscillations but dropping the low-frequency ones?)

      Well, you are getting there... Basically, a lot of the actual processing of discrete sample sequences (such as a .wav file) are based on an operation called a convolution (which is the same as a correlation with one argument reversed in time). Thus, you "convolve" a digital sequence with another, usually much shorter sequence that is known as a filter. The filter sequence is deisgned to be low-pass, high-pass, band-pass, etc.

      The key point is that a convolution in the time domain (ie. dealing with discrete samples in time) has an equivalence to operations in the frequency domain (what you get when you do a Fourier Transform on a sequence of time samples). By designing your filter in the frequency domain, you come up with filter sequences in the time domain, and the "convolution" does the rest. It is really some of the most elegant mathematics one can study in an engineering discipline (IMO)

      Anyway, this oversimplifies many things, but is a step toward removing the "mystery" of digital filtering. I would suggest a book such as Hamming's "Digital Filters" (which is cheap and good), or checking Google for "Fourier digital filtering", etc. for more info. You were on to something when you talked about averaging; that is indeed a simple low-pass filter, but much better ones can be made almost as simply.

      BTW, wavelets are a related topic, but it is probably better to understand the Fourier realm first.

      I welcome any corrections or clarifications to these statements by those in the know; or follow up questions if what I said is unclear.

    • Probably the best source (ahem) for real-world working signal-processing code, for bit-twiddling type people, is SoX [sourceforge.net], which has been vastly improved by its current maintainer Chris Bagwell who took it over in 1995. It includes a few different filtering algorithms (see band.c, deemphas.c, earwax.c, filter.c, highp.c, lowp.c) among other changes (no more clicking noises when resampling, hooray!).

  • sound processing (Score:3, Informative)

    by bedessen ( 411686 ) on Friday March 22, 2002 @04:35PM (#3209872) Journal
    To get a signal shifted by a uniform 90 degress across all frequencies, I believe you want to use the Hilbert Transform [wolfram.com].

    As far as applications, if you're using windows, there are a number of very advanced audio editor programs, like Cool Edit, Goldwave, etc. Try a search at one of the usual software download places.

    Probably the most extensive DSP applications turn to something like Matlab, but that's hardly free.

    For a unix-style solution, there's a package of tools here [cf.ac.uk] called pipewave which allows you to do very complicated digital signal processing using unix pipes. It follows the philosophy that you can do very complicated tasks by cascading smaller components using pipelines. Here are some examples from the above site:


    Plot the Fourier transform of a file using a Hanning window.

    fd file | fft -h | plot

    Generate the default synthestic vowel (200 ms duration, fundamental frequency = 100 Hz and formants, F1 = 650 Hz and F2 = 950 Hz), halve the sampling rate, take the hanning-windowed Fourier transform, and plot

    klatt -k | downsample 2 | fft -h | plot

    Generate 2000 samples of white noise, pass it through a gamma-tone filterbank (1 channel per ERB), arrange the filtered waveforms in a cascade, such that there is no overlap between them and plot.

    gaussian 2000 | fbank -e 1 | cascade -o 0.8 | plot -b

    Generate 2000 samples of white noise, store a copy of the original in a file called 'noise0', then phase shift it by 90 and store the phase shifted version as 'noise90'.

    gaussian 2000 | store -o noise0 | fft -p | ffilt -a 90 | ift | store noise90

  • Anyone know when it will be ready for use? Thanks,

    David
  • question... (Score:2, Interesting)

    by superflex ( 318432 )
    Near the beginning of the article, there were a couple of references to Fourier analysis being ill-equipped to deal with transient components in signals. It also mentions this being related to the Heisengberg Uncertainty Principle...

    My first impression when they mentioned "transients" and "crappy" was the Gibbs phenomenon. Is this what they were talking about, and more importantly, is the Gibbs phenomenon caused by the H.U.P.?

    • Near the beginning of the article, there were a couple of references to Fourier analysis being ill-equipped to deal with transient components in signals. It also mentions this being related to the Heisengberg Uncertainty Principle...

      My first impression when they mentioned "transients" and "crappy" was the Gibbs phenomenon. Is this what they were talking about, and more importantly, is the Gibbs phenomenon caused by the H.U.P.?

      It's the other way around; HUP (which is physics) is caused by the fact that you can't extrapolate the full shape of a wave from a small piece of it (which is math).

      A good general rule of thumb: there are many examples of math causing physics, but there are no known cases of physics causing math.

      -- MarkusQ

  • Ok, I'm sold on wavelets. Now, which browsers can decompress a JPEG-2000 file? And where can I get an app that allows me to take an image and "Save as..." JPEG-2000 format?

    (Preferably a cheap shareware app. Heh)
  • This article was interestingly devoid of information. Let's us know they're really simple, e.g.:
    The Daubechies wavelets turn the theory into a practical tool that can be easily programmed and used by any scientist with a minimum of mathematical training.
    But apparently not so simple they could explain any of it to us blockheads.

    Anybody know where you could find a introduction to this subject that actually introduces it?

Solutions are obvious if one only has the optical power to observe them over the horizon. -- K.A. Arsdall

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