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’ve corrected the URL, which points to the Wikipedia article, and that had an extra character in it.)

The responses, in the order received:

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

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

cparasat @dweinberger It’s just adding waves to other waves.v

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

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

JoeAndrieu @dweinberger 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 @dweinberger in 1 tweet?! impossible

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

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

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

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

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

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

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

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

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

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

_eon_ @dweinberger think of waves on the ocea

[Tags: fourier twitter ]