## 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:

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

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

cparasatIt’s just adding waves to other waves.

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

fantomplanetFourier Xformations are like ironing your shirt. It smooths things out.

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

ts_eliotin 1 tweet?! impossible

fanfthe 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.

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

ricklevineOf 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.

fieldsThings 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.

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

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

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

vnitinevery 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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