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Fourier Tweet Transforms

Me, on Twitter:

Challenge: Explain Fourier Transforms, w/o math, to a Humanities major (me), more clearly than http://tinyurl.com/27n3g … in 1 tweet?

Note: I somehow got the TinyURL, which points to the Wikipedia article, wrong. The Wikipedia article begins this way: “In mathematics, the Fourier transform is an operation that transforms one complex-valued function of a real variable into another.” It does not get clearer after that, at least for this Humanities major.


The responses, in the order received:

jonathanweber Looking at a periodic signal in time, the Fourier transform explains it in terms of what mix of frequencies is present. Helps?

DarrylParker the better question is why does a humanities major like you need to understand it? ;)

cparasat It’s just adding waves to other waves.

DarrylParker simpler overview of the Fourier series, but still a bit mathematical – http://tinyurl.com/chw5pp

fantomplanet Fourier Xformations are like ironing your shirt. It smooths things out.

JoeAndrieu FTs take a signal in time and represent it as a series of frequencies. Makes audio signal look like an equalizer graph.v

ts_eliot in 1 tweet?! impossible

fanf the FT splits a signal into separate frequencies, like a prism splits light

fjania – it shows us which, and how much of each, simple sine waves we can add together to reconstruct the signal we’re transforming.

ricklevine Hm. Fourier transforms convert a bunch of sample measurements (audio, seismic data, etc) into frequency info: http://is.gd/iQP3

ricklevine Of course there’s a lot more to it. Try: it’s a way of taking seemingly rndm data and fitting a curve to it, enabling analysis.

fields Things you don’t understand can be expressed in smaller equivalent pieces of things you don’t understand.

IanYorston “Explain Fourier Transforms to a Humanities major”. Smart maths breaks large constructs down into small things loosely joined.

mtobis: your tinyurl fails. Fourier transform an audio signal and get back an amplitude for each pure tone; no information is lost.

vasusrini Sound=Vibrating Air.Bee Buzz & dog bark=diff. frequency signatures. Ear hears all at once & sorts it. FT is the m/c equivalent.

chichiri just say without it you wouldn’t have JPEGs, enough said ;)

artficlinanity Every signal, no matter how complex, is made up of simple sinusoids. Fourier Transformation is how you find those.

vnitin every physical phenomenon can be viewed as existing in space+time or vibrations+energy. FourierTrfm converts view1 -> view2

_eon_ think of waves on the ocean

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