The Discrete Fourier Transform (DFT) Next  |  Prev  |  Up  |  Top  |  Index  |  JOS Index  |  JOS Pubs  |  JOS Home  |  Search

The Discrete Fourier Transform (DFT)

Given a signal $ x(\cdot)\in{\bf C}^N$, its DFT is defined by6.3

$\displaystyle X(\omega_k) \isdef \left<x,s_k\right> \isdef \sum_{n=0}^{N-1}x(n) \overline{s_k(n)},
\quad k=0,1,2,\ldots,N-1,


$\displaystyle s_k(n)\isdef e^{j\omega_k t_n},
\quad t_n\isdef nT,
\quad \omega_k\isdef 2\pi\frac{k}{N}f_s,
\quad f_s\isdef \frac{1}{T},

or, as it is most often written,

$\displaystyle \zbox {X(\omega_k) \isdef \sum_{n=0}^{N-1}x(n) e^{-j\frac{2\pi k n}{N}},
\quad k=0,1,2,\ldots,N-1.}

We may also refer to $ X$ as the spectrum of $ x$, and $ X(\omega_k)$ is the $ k$th sample of the spectrum at frequency $ \omega_k$. Thus, the $ k$th sample $ X(\omega_k)$ of the spectrum of $ x$ is defined as the inner product of $ x$ with the $ k$th DFT sinusoid $ s_k$. This definition is $ N$ times the coefficient of projection of $ x$ onto $ s_k$, i.e.,

$\displaystyle \frac{\left<x,s_k\right>}{\left\Vert\,s_k\,\right\Vert^2} = \frac{X(\omega_k)}{N}.

The projection of $ x$ onto $ s_k$ is

$\displaystyle {\bf P}_{s_k}(x) = \frac{X(\omega_k)}{N} s_k.

Since the $ \{s_k\}$ are orthogonal and span $ {\bf C}^N$, using the main result of the preceding chapter, we have that the inverse DFT is given by the sum of the projections

$\displaystyle x = \sum_{k=0}^{N-1}\frac{X(\omega_k)}{N} s_k,

or, as we normally write,

$\displaystyle \zbox {x(n) = \frac{1}{N} \sum_{k=0}^{N-1}X(\omega_k) e^{j\frac{2\pi k n}{N}}, \quad n=0,1,\ldots,N-1.}

In summary, the DFT is proportional to the set of coefficients of projection onto the sinusoidal basis set, and the inverse DFT is the reconstruction of the original signal as a superposition of its sinusoidal projections. This basic ``architecture'' extends to all linear orthogonal transforms, including wavelets, Fourier transforms, Fourier series, the discrete-time Fourier transform (DTFT), and certain short-time Fourier transforms (STFT). See Appendix B for some of these.

We have defined the DFT from a geometric signal theory point of view, building on the preceding chapter. See §7.1.1 for notation and terminology associated with the DFT.

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``Mathematics of the Discrete Fourier Transform (DFT), with Music and Audio Applications'', by Julius O. Smith III, W3K Publishing, 2003, ISBN 0-9745607-0-7.
Copyright © 2007-02-02 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA),   Stanford University
CCRMA  [Automatic-links disclaimer]