Difference between revisions of "Fast Fourier Transform"
From Hydrogenaudio Knowledgebase
(11 intermediate revisions by 6 users not shown) | |||
Line 1: | Line 1: | ||
− | + | '''Fast Fourier transform''' ('''FFT''') is an efficient algorithm for calculating the [[DFT|discrete Fourier transform]] (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of <math>O(n^2)\,</math> computations, FFT takes an order of <math>O(n\,\log\,n)</math>, and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a ''FFT Radix 2'' algorithm where <math> n = 64,128,256,512,1024,2048</math> etc. | |
+ | Unlike the DFT, the frequency spacing nor window size per-bin can't be adjusted because FFT bins aren't calculated individually. | ||
− | Fast Fourier | + | ==External links== |
− | + | * {{wikipedia|Fast Fourier transform}} | |
+ | [[Category:Signal Processing]] | ||
[[Category:Technical]] | [[Category:Technical]] | ||
− |
Revision as of 13:01, 22 March 2022
Fast Fourier transform (FFT) is an efficient algorithm for calculating the discrete Fourier transform (DFT). The FFT produces the same results as a DFT but it reduces the execution time by hundreds in some cases. Whereas DFT takes an order of computations, FFT takes an order of , and is definitely the preferred algorithm to be used in all applications in terms of computational complexity. The FFT in most implementations consistent of samples that are exactly a power of 2, this is commonly known as a FFT Radix 2 algorithm where etc.
Unlike the DFT, the frequency spacing nor window size per-bin can't be adjusted because FFT bins aren't calculated individually.
External links
- Fast Fourier transform on Wikipedia