Discrete-time Fourier transform

Not to be confused with the discrete Fourier transform.

In mathematics, the discrete-time Fourier transform (DTFT) is a form of Fourier analysis that is applicable to a sequence of discrete values.

The DTFT is often used to analyze samples of a continuous function. The term discrete-time refers to the fact that the transform operates on discrete data, often samples whose interval has units of time. From uniformly spaced samples it produces a function of frequency that is a periodic summation of the continuous Fourier transform of the original continuous function. Under certain theoretical conditions, described by the sampling theorem, the original continuous function can be recovered perfectly from the DTFT and thus from the original discrete samples. The DTFT itself is a continuous function of frequency, but discrete samples of it can be readily calculated via the discrete Fourier transform (DFT) (see § Sampling the DTFT), which is by far the most common method of modern Fourier analysis.

Both transforms are invertible. The inverse DTFT is the original sampled data sequence. The inverse DFT is a periodic summation of the original sequence. The fast Fourier transform (FFT) is an algorithm for computing one cycle of the DFT, and its inverse produces one cycle of the inverse DFT.

Introduction

Relation to Fourier Transform

We begin with a common definition of the Fourier transform integral:

${\displaystyle X(f)\triangleq \int _{-\infty }^{\infty }x(t)\cdot e^{-i2\pi ft}dt.}$ 

This reduces to a summation (see Fourier transform § Numerical integration of a series of ordered pairs) when ${\displaystyle x(t)}$  is replaced by a discrete sequence of its samples, ${\displaystyle x(nT),}$  for integer values of ${\displaystyle n.}$   ${\displaystyle dt}$  is also replaced by ${\displaystyle T,}$  leaving:

${\displaystyle X_{1/T}(f)\triangleq \sum _{n=-\infty }^{\infty }\underbrace {T\cdot x(nT)} _{x[n]}\ e^{-i2\pi fTn},}$ 

which is a Fourier series in frequency, with periodicity ${\displaystyle 1/T.}$  The subscript ${\displaystyle 1/T}$  distinguishes it from ${\displaystyle X(f)}$  and from the angular frequency form of the DTFT. I.e., when the frequency variable, ${\displaystyle \omega ,}$  has normalized units of radians/sample, the periodicity is ${\displaystyle 2\pi ,}$  and the Fourier series is:^[1]: p.147

${\displaystyle X_{2\pi }(\omega )=X_{1/T}\left({\tfrac {\omega }{2\pi T}}\right)=\sum _{n=-\infty }^{\infty }x[n]\cdot e^{-i\omega n}.}$

(Eq.1)

 

Fig 1. Depiction of a Fourier transform (upper left) and its periodic summation (DTFT) in the lower left corner. The lower right corner depicts samples of the DTFT that are computed by a discrete Fourier transform (DFT).

The utility of the DTFT is rooted in the Poisson summation formula, which tells us that the periodic function represented by the Fourier series is a periodic summation of the Fourier transform:^[a][A]

Poisson summation

${\displaystyle X_{1/T}(f)=\sum _{n=-\infty }^{\infty }x[n]\cdot e^{-i2\pi fTn}\;=\sum _{k=-\infty }^{\infty }X\left(f-k/T\right).}$

(Eq.2)

The integer ${\displaystyle k}$  has units of cycles/sample, and ${\displaystyle 1/T}$  is the sample-rate, ${\displaystyle f_{s}}$  (samples/sec).  So ${\displaystyle X_{1/T}(f)}$  comprises exact copies of ${\displaystyle X(f)}$  that are shifted by multiples of ${\displaystyle f_{s}}$  hertz and combined by addition. For sufficiently large ${\displaystyle f_{s}}$  the ${\displaystyle k=0}$  term can be observed in the region ${\displaystyle [-f_{s}/2,f_{s}/2]}$  with little or no distortion (aliasing) from the other terms.  Fig.1 depicts an example where ${\displaystyle 1/T}$  is not large enough to prevent aliasing.

We also note that ${\displaystyle e^{-i2\pi fTn}}$  is the Fourier transform of ${\displaystyle \delta (t-nT).}$  Therefore, an alternative definition of DTFT is:^[B]

${\displaystyle X_{1/T}(f)={\mathcal {F}}\left\{\sum _{n=-\infty }^{\infty }x[n]\cdot \delta (t-nT)\right\}.}$

(Eq.3)

The modulated Dirac comb function is a mathematical abstraction sometimes referred to as impulse sampling.^[3]

Inverse transform

An operation that recovers the discrete data sequence from the DTFT function is called an inverse DTFT. For instance, the inverse continuous Fourier transform of both sides of Eq.3 produces the sequence in the form of a modulated Dirac comb function:

${\displaystyle \sum _{n=-\infty }^{\infty }x[n]\cdot \delta (t-nT)={\mathcal {F}}^{-1}\left\{X_{1/T}(f)\right\}\ \triangleq \int _{-\infty }^{\infty }X_{1/T}(f)\cdot e^{i2\pi ft}df.}$ 

However, noting that ${\displaystyle X_{1/T}(f)}$  is periodic, all the necessary information is contained within any interval of length ${\displaystyle 1/T.}$   In both Eq.1 and Eq.2, the summations over ${\displaystyle n}$  are a Fourier series, with coefficients ${\displaystyle x[n].}$   The standard formulas for the Fourier coefficients are also the inverse transforms:

${\displaystyle {\begin{aligned}x[n]&=T\int _{\frac {1}{T}}X_{1/T}(f)\cdot e^{i2\pi fnT}df\quad \scriptstyle {{\text{(integral over any interval of length }}1/T{\textrm {)}}}\\\displaystyle &={\frac {1}{2\pi }}\int _{2\pi }X_{2\pi }(\omega )\cdot e^{i\omega n}d\omega \quad \scriptstyle {{\text{(integral over any interval of length }}2\pi {\textrm {)}}}\end{aligned}}}$

(Eq.4)

Periodic data

When the input data sequence ${\displaystyle x[n]}$  is ${\displaystyle N}$ -periodic, Eq.2 can be computationally reduced to a discrete Fourier transform (DFT), because:

• All the available information is contained within ${\displaystyle N}$  samples.
• ${\displaystyle X_{1/T}(f)}$  converges to zero everywhere except at integer multiples of ${\displaystyle 1/(NT),}$  known as harmonic frequencies. At those frequencies, the DTFT diverges at different frequency-dependent rates. And those rates are given by the DFT of one cycle of the ${\displaystyle x[n]}$  sequence.
• The DTFT is periodic, so the maximum number of unique harmonic amplitudes is ${\displaystyle (1/T)/(1/(NT))=N.}$ 

The DFT of one cycle of the ${\displaystyle x[n]}$  sequence is:

${\displaystyle X[k]\triangleq \underbrace {\sum _{N}x[n]\cdot e^{-i2\pi {\frac {k}{N}}n}} _{\text{any n-sequence of length N}},\quad k\in \mathbf {Z} .}$ 

And ${\displaystyle x[n]}$  can be expressed in terms of the inverse transform:

${\displaystyle x[n]={\frac {1}{N}}\underbrace {\sum _{N}X[k]\cdot e^{i2\pi {\frac {k}{N}}n}} _{\text{any k-sequence of length N}},\quad n\in \mathbf {Z} .}$ 

The inverse DFT is sometimes referred to as a Discrete Fourier series (DFS).^{[1]: p 542}

${\displaystyle {\begin{aligned}X_{1/T}(f)&\triangleq \sum _{n=-\infty }^{\infty }x[n]\cdot e^{-i2\pi fnT}\\&=\sum _{n=-\infty }^{\infty }\left[{\frac {1}{N}}\sum _{k=0}^{N-1}X[k]\cdot e^{i2\pi {\frac {k}{N}}n}\right]\cdot e^{-i2\pi fnT}\\&={\frac {1}{N}}\sum _{k=0}^{N-1}X[k]\underbrace {\left[\sum _{n=-\infty }^{\infty }e^{i2\pi {\frac {k}{N}}n}\cdot e^{-i2\pi fnT}\right]} _{\operatorname {DTFT} \left(e^{i2\pi {\frac {k}{N}}n}\right)}\\&={\frac {1}{N}}\sum _{k=0}^{N-1}X[k]\cdot {\frac {1}{T}}\sum _{M=-\infty }^{\infty }\delta \left(f-{\tfrac {k}{NT}}-{\tfrac {M}{T}}\right)\end{aligned}}}$       ^[b]

Due to the ${\displaystyle N}$ -periodicity of both functions of ${\displaystyle k,}$  this can be simplified to:

${\displaystyle X_{1/T}(f)={\frac {1}{NT}}\sum _{k=-\infty }^{\infty }X[k]\cdot \delta \left(f-{\frac {k}{NT}}\right),}$ 

which satisfies the inverse transform requirement:

${\displaystyle {\begin{aligned}x[n]&=T\int _{0}^{\frac {1}{T}}X_{1/T}(f)\cdot e^{i2\pi fnT}df\\&={\frac {1}{N}}\sum _{k=-\infty }^{\infty }X[k]\underbrace {\int _{0}^{\frac {1}{T}}\delta \left(f-{\tfrac {k}{NT}}\right)e^{i2\pi fnT}df} _{{\text{zero for }}k\ \notin \ [0,N-1]}\\&={\frac {1}{N}}\sum _{k=0}^{N-1}X[k]\int _{0}^{\frac {1}{T}}\delta \left(f-{\tfrac {k}{NT}}\right)e^{i2\pi fnT}df\\&={\frac {1}{N}}\sum _{k=0}^{N-1}X[k]\cdot e^{i2\pi {\tfrac {k}{NT}}nT}\\&={\frac {1}{N}}\sum _{k=0}^{N-1}X[k]\cdot e^{i2\pi {\tfrac {k}{N}}n}\end{aligned}}}$ 

Sampling the DTFT

When the DTFT is continuous, a common practice is to compute an arbitrary number of samples ${\displaystyle (N)}$  of one cycle of the periodic function ${\displaystyle X_{1/T}}$ : ^{[1]: pp 557–559 & 703}

${\displaystyle {\begin{aligned}\underbrace {X_{1/T}\left({\frac {k}{NT}}\right)} _{X_{k}}&=\sum _{n=-\infty }^{\infty }x[n]\cdot e^{-i2\pi {\frac {k}{N}}n}\quad \quad k=0,\dots ,N-1\\&=\underbrace {\sum _{N}x_{_{N}}[n]\cdot e^{-i2\pi {\frac {k}{N}}n},} _{\text{DFT}}\quad \scriptstyle {{\text{(sum over any }}n{\text{-sequence of length }}N)}\end{aligned}}}$ 

where ${\displaystyle x_{_{N}}}$  is a periodic summation:

${\displaystyle x_{_{N}}[n]\ \triangleq \ \sum _{m=-\infty }^{\infty }x[n-mN].}$      (see Discrete Fourier series)

The ${\displaystyle x_{_{N}}}$  sequence is the inverse DFT. Thus, our sampling of the DTFT causes the inverse transform to become periodic. The array of ${\displaystyle |X_{k}|^{2}}$  values is known as a periodogram, and the parameter ${\displaystyle N}$  is called NFFT in the Matlab function of the same name.^[4]

In order to evaluate one cycle of ${\displaystyle x_{_{N}}}$  numerically, we require a finite-length ${\displaystyle x[n]}$  sequence. For instance, a long sequence might be truncated by a window function of length ${\displaystyle L}$  resulting in three cases worthy of special mention. For notational simplicity, consider the ${\displaystyle x[n]}$  values below to represent the values modified by the window function.

Case: Frequency decimation. ${\displaystyle L=N\cdot I,}$  for some integer ${\displaystyle I}$  (typically 6 or 8)

A cycle of ${\displaystyle x_{_{N}}}$  reduces to a summation of ${\displaystyle I}$  segments of length ${\displaystyle N.}$   The DFT then goes by various names, such as:

• window-presum FFT^[5]
• Weight, overlap, add (WOLA)^{[6][7][8][9][10][11][C][D]}

• polyphase DFT^[9][10]
• polyphase filter bank^[12]
• multiple block windowing and time-aliasing.^[13]

Recall that decimation of sampled data in one domain (time or frequency) produces overlap (sometimes known as aliasing) in the other, and vice versa. Compared to an ${\displaystyle L}$ -length DFT, the ${\displaystyle x_{_{N}}}$  summation/overlap causes decimation in frequency,^[1]: p.558 leaving only DTFT samples least affected by spectral leakage. That is usually a priority when implementing an FFT filter-bank (channelizer). With a conventional window function of length ${\displaystyle L,}$  scalloping loss would be unacceptable. So multi-block windows are created using FIR filter design tools.^[14][15]  Their frequency profile is flat at the highest point and falls off quickly at the midpoint between the remaining DTFT samples. The larger the value of parameter ${\displaystyle I,}$  the better the potential performance.

Case: ${\displaystyle L=N+1}$ 

When a symmetric, ${\displaystyle L}$ -length window function (${\displaystyle x}$ ) is truncated by 1 coefficient it is called periodic or DFT-even. That is a common practice, but the truncation affects the DTFT (spectral leakage) by a small amount. It is at least of academic interest to characterize that effect.  An ${\displaystyle N}$ -length DFT of the truncated window produces frequency samples at intervals of ${\displaystyle 1/N,}$  instead of ${\displaystyle 1/L.}$   The samples are real-valued,^[16]: p.52  but their values do not exactly match the DTFT of the symmetric window. The periodic summation, ${\displaystyle x_{_{N}},}$  along with an ${\displaystyle N}$ -length DFT, can also be used to sample the DTFT at intervals of ${\displaystyle 1/N.}$   Those samples are also real-valued and do exactly match the DTFT (example: File:Sampling the Discrete-time Fourier transform.svg). To use the full symmetric window for spectral analysis at the ${\displaystyle 1/N}$  spacing, one would combine the ${\displaystyle n=0}$  and ${\displaystyle n=N}$  data samples (by addition, because the symmetrical window weights them equally) and then apply the truncated symmetric window and the ${\displaystyle N}$ -length DFT.

 

Fig 2. DFT of e^i2πn/8 for L = 64 and N = 256

 

Fig 3. DFT of e^i2πn/8 for L = 64 and N = 64

Case: Frequency interpolation. ${\displaystyle L\leq N}$ 

In this case, the DFT simplifies to a more familiar form:

${\displaystyle X_{k}=\sum _{n=0}^{N-1}x[n]\cdot e^{-i2\pi {\frac {k}{N}}n}.}$ 

In order to take advantage of a fast Fourier transform algorithm for computing the DFT, the summation is usually performed over all ${\displaystyle N}$  terms, even though ${\displaystyle N-L}$  of them are zeros. Therefore, the case ${\displaystyle L<N}$  is often referred to as zero-padding.

Spectral leakage, which increases as ${\displaystyle L}$  decreases, is detrimental to certain important performance metrics, such as resolution of multiple frequency components and the amount of noise measured by each DTFT sample. But those things don't always matter, for instance when the ${\displaystyle x[n]}$  sequence is a noiseless sinusoid (or a constant), shaped by a window function. Then it is a common practice to use zero-padding to graphically display and compare the detailed leakage patterns of window functions. To illustrate that for a rectangular window, consider the sequence:

${\displaystyle x[n]=e^{i2\pi {\frac {1}{8}}n},\quad }$  and ${\displaystyle L=64.}$ 

Figures 2 and 3 are plots of the magnitude of two different sized DFTs, as indicated in their labels. In both cases, the dominant component is at the signal frequency: ${\displaystyle f=1/8=0.125}$ . Also visible in Fig 2 is the spectral leakage pattern of the ${\displaystyle L=64}$  rectangular window. The illusion in Fig 3 is a result of sampling the DTFT at just its zero-crossings. Rather than the DTFT of a finite-length sequence, it gives the impression of an infinitely long sinusoidal sequence. Contributing factors to the illusion are the use of a rectangular window, and the choice of a frequency (1/8 = 8/64) with exactly 8 (an integer) cycles per 64 samples. A Hann window would produce a similar result, except the peak would be widened to 3 samples (see DFT-even Hann window).

Convolution

Main article: Convolution theorem § Functions of a discrete variable (sequences)

The convolution theorem for sequences is:

${\displaystyle x*y\ =\ \scriptstyle {\rm {DTFT}}^{-1}\displaystyle \left[\scriptstyle {\rm {DTFT}}\displaystyle \{x\}\cdot \scriptstyle {\rm {DTFT}}\displaystyle \{y\}\right].}$ ^{[17]: p.297 [c]}

An important special case is the circular convolution of sequences x and y defined by ${\displaystyle x_{_{N}}*y,}$  where ${\displaystyle x_{_{N}}}$  is a periodic summation. The discrete-frequency nature of ${\displaystyle \scriptstyle {\rm {DTFT}}\displaystyle \{x_{_{N}}\}}$  means that the product with the continuous function ${\displaystyle \scriptstyle {\rm {DTFT}}\displaystyle \{y\}}$  is also discrete, which results in considerable simplification of the inverse transform:

${\displaystyle x_{_{N}}*y\ =\ \scriptstyle {\rm {DTFT}}^{-1}\displaystyle \left[\scriptstyle {\rm {DTFT}}\displaystyle \{x_{_{N}}\}\cdot \scriptstyle {\rm {DTFT}}\displaystyle \{y\}\right]\ =\ \scriptstyle {\rm {DFT}}^{-1}\displaystyle \left[\scriptstyle {\rm {DFT}}\displaystyle \{x_{_{N}}\}\cdot \scriptstyle {\rm {DFT}}\displaystyle \{y_{_{N}}\}\right].}$ ^{[18][1]: p.548}

For x and y sequences whose non-zero duration is less than or equal to N, a final simplification is:

${\displaystyle x_{_{N}}*y\ =\ \scriptstyle {\rm {DFT}}^{-1}\displaystyle \left[\scriptstyle {\rm {DFT}}\displaystyle \{x\}\cdot \scriptstyle {\rm {DFT}}\displaystyle \{y\}\right].}$ 

The significance of this result is explained at Circular convolution and Fast convolution algorithms.

Symmetry properties

When the real and imaginary parts of a complex function are decomposed into their even and odd parts, there are four components, denoted below by the subscripts RE, RO, IE, and IO. And there is a one-to-one mapping between the four components of a complex time function and the four components of its complex frequency transform:^{[17]: p.291}

${\displaystyle {\begin{aligned}{\mathsf {Time\ domain}}\quad &\ x\quad &=\quad &x_{_{RE}}\quad &+\quad &x_{_{RO}}\quad &+\quad i\ &x_{_{IE}}\quad &+\quad &\underbrace {i\ x_{_{IO}}} \\&{\Bigg \Updownarrow }{\mathcal {F}}&&{\Bigg \Updownarrow }{\mathcal {F}}&&\ \ {\Bigg \Updownarrow }{\mathcal {F}}&&\ \ {\Bigg \Updownarrow }{\mathcal {F}}&&\ \ {\Bigg \Updownarrow }{\mathcal {F}}\\{\mathsf {Frequency\ domain}}\quad &X\quad &=\quad &X_{RE}\quad &+\quad &\overbrace {i\ X_{IO}} \quad &+\quad i\ &X_{IE}\quad &+\quad &X_{RO}\end{aligned}}}$ 

From this, various relationships are apparent, for example:

• The transform of a real-valued function ${\displaystyle (x_{_{RE}}+x_{_{RO}})}$  is the even symmetric function ${\displaystyle X_{RE}+i\ X_{IO}.}$  Conversely, an even-symmetric transform implies a real-valued time-domain.
• The transform of an imaginary-valued function ${\displaystyle (i\ x_{_{IE}}+i\ x_{_{IO}})}$  is the odd symmetric function ${\displaystyle X_{RO}+i\ X_{IE},}$  and the converse is true.
• The transform of an even-symmetric function ${\displaystyle (x_{_{RE}}+i\ x_{_{IO}})}$  is the real-valued function ${\displaystyle X_{RE}+X_{RO},}$  and the converse is true.
• The transform of an odd-symmetric function ${\displaystyle (x_{_{RO}}+i\ x_{_{IE}})}$  is the imaginary-valued function ${\displaystyle i\ X_{IE}+i\ X_{IO},}$  and the converse is true.

Relationship to the Z-transform

${\displaystyle X_{2\pi }(\omega )}$  is a Fourier series that can also be expressed in terms of the bilateral Z-transform.  I.e.:

${\displaystyle X_{2\pi }(\omega )=\left.{\widehat {X}}(z)\,\right|_{z=e^{i\omega }}={\widehat {X}}(e^{i\omega }),}$ 

where the ${\displaystyle {\widehat {X}}}$  notation distinguishes the Z-transform from the Fourier transform. Therefore, we can also express a portion of the Z-transform in terms of the Fourier transform:

${\displaystyle {\begin{aligned}{\widehat {X}}(e^{i\omega })&=\ X_{1/T}\left({\tfrac {\omega }{2\pi T}}\right)\ =\ \sum _{k=-\infty }^{\infty }X\left({\tfrac {\omega }{2\pi T}}-k/T\right)\\&=\sum _{k=-\infty }^{\infty }X\left({\tfrac {\omega -2\pi k}{2\pi T}}\right).\end{aligned}}}$ 

Note that when parameter T changes, the terms of ${\displaystyle X_{2\pi }(\omega )}$  remain a constant separation ${\displaystyle 2\pi }$  apart, and their width scales up or down. The terms of X_1/T(f) remain a constant width and their separation 1/T scales up or down.

Table of discrete-time Fourier transforms

Some common transform pairs are shown in the table below. The following notation applies:

• ${\displaystyle \omega =2\pi fT}$  is a real number representing continuous angular frequency (in radians per sample). (${\displaystyle f}$  is in cycles/sec, and ${\displaystyle T}$  is in sec/sample.) In all cases in the table, the DTFT is 2π-periodic (in ${\displaystyle \omega }$ ).
• ${\displaystyle X_{2\pi }(\omega )}$  designates a function defined on ${\displaystyle -\infty <\omega <\infty }$ .
• ${\displaystyle X_{o}(\omega )}$  designates a function defined on ${\displaystyle -\pi <\omega \leq \pi }$ , and zero elsewhere. Then:
  $${\displaystyle X_{2\pi }(\omega )\ \triangleq \sum _{k=-\infty }^{\infty }X_{o}(\omega -2\pi k).}$$

   
• ${\displaystyle \delta (\omega )}$  is the Dirac delta function
• ${\displaystyle \operatorname {sinc} (t)}$  is the normalized sinc function
• ${\displaystyle \operatorname {rect} \left[{n \over L}\right]\triangleq {\begin{cases}1&|n|\leq L/2\\0&|n|>L/2\end{cases}}}$ 
• ${\displaystyle \operatorname {tri} (t)}$  is the triangle function
• n is an integer representing the discrete-time domain (in samples)
• ${\displaystyle u[n]}$  is the discrete-time unit step function
• ${\displaystyle \delta [n]}$  is the Kronecker delta ${\displaystyle \delta _{n,0}}$ 

Time domain x[n]	Frequency domain X2π(ω)	Remarks	Reference
${\displaystyle \delta [n]}$	${\displaystyle X_{2\pi }(\omega )=1}$		^[17]^: p.305
${\displaystyle \delta [n-M]}$	${\displaystyle X_{2\pi }(\omega )=e^{-i\omega M}}$	integer ${\displaystyle M}$
${\displaystyle \sum _{m=-\infty }^{\infty }\delta [n-Mm]\!}$	${\displaystyle X_{2\pi }(\omega )=\sum _{m=-\infty }^{\infty }e^{-i\omega Mm}={\frac {2\pi }{M}}\sum _{k=-\infty }^{\infty }\delta \left(\omega -{\frac {2\pi k}{M}}\right)\,}$  ${\displaystyle X_{o}(\omega )={\frac {2\pi }{M}}\sum _{k=-(M-1)/2}^{(M-1)/2}\delta \left(\omega -{\frac {2\pi k}{M}}\right)\,}$      odd M ${\displaystyle X_{o}(\omega )={\frac {2\pi }{M}}\sum _{k=-M/2+1}^{M/2}\delta \left(\omega -{\frac {2\pi k}{M}}\right)\,}$      even M	integer ${\displaystyle M>0}$
${\displaystyle u[n]}$	${\displaystyle X_{2\pi }(\omega )={\frac {1}{1-e^{-i\omega }}}+\pi \sum _{k=-\infty }^{\infty }\delta (\omega -2\pi k)\!}$  ${\displaystyle X_{o}(\omega )={\frac {1}{1-e^{-i\omega }}}+\pi \cdot \delta (\omega )\!}$	The ${\displaystyle 1/(1-e^{-i\omega })}$  term must be interpreted as a distribution in the sense of a Cauchy principal value around its poles at ${\displaystyle \omega =2\pi k}$ .
${\displaystyle a^{n}u[n]}$	${\displaystyle X_{2\pi }(\omega )={\frac {1}{1-ae^{-i\omega }}}\!}$	${\displaystyle 0<|a|<1}$	^[17]^: p.305
${\displaystyle e^{-ian}}$	${\displaystyle X_{o}(\omega )=2\pi \cdot \delta (\omega +a),}$      -π < a < π ${\displaystyle X_{2\pi }(\omega )=2\pi \sum _{k=-\infty }^{\infty }\delta (\omega +a-2\pi k)}$	real number ${\displaystyle a}$
${\displaystyle \cos(a\cdot n)}$	${\displaystyle X_{o}(\omega )=\pi \left[\delta \left(\omega -a\right)+\delta \left(\omega +a\right)\right],}$  ${\displaystyle X_{2\pi }(\omega )\ \triangleq \sum _{k=-\infty }^{\infty }X_{o}(\omega -2\pi k)}$	real number ${\displaystyle a}$  with ${\displaystyle -\pi <a<\pi }$
${\displaystyle \sin(a\cdot n)}$	${\displaystyle X_{o}(\omega )={\frac {\pi }{i}}\left[\delta \left(\omega -a\right)-\delta \left(\omega +a\right)\right]}$	real number ${\displaystyle a}$  with ${\displaystyle -\pi <a<\pi }$
${\displaystyle \operatorname {rect} \left[{n-M \over N}\right]\equiv \operatorname {rect} \left[{n-M \over N-1}\right]}$	${\displaystyle X_{o}(\omega )={\sin(N\omega /2) \over \sin(\omega /2)}\,e^{-i\omega M}\!}$	integer ${\displaystyle M,}$  and odd integer ${\displaystyle N}$
${\displaystyle \operatorname {sinc} (W(n+a))}$	${\displaystyle X_{o}(\omega )={\frac {1}{W}}\operatorname {rect} \left({\omega \over 2\pi W}\right)e^{ia\omega }}$	real numbers ${\displaystyle W,a}$  with ${\displaystyle 0<W<1}$
${\displaystyle \operatorname {sinc} ^{2}(Wn)\,}$	${\displaystyle X_{o}(\omega )={\frac {1}{W}}\operatorname {tri} \left({\omega \over 2\pi W}\right)}$	real number ${\displaystyle W}$ , ${\displaystyle 0<W<0.5}$
${\displaystyle {\begin{cases}0&n=0\\{\frac {(-1)^{n}}{n}}&{\text{elsewhere}}\end{cases}}}$	${\displaystyle X_{o}(\omega )=j\omega }$	it works as a differentiator filter
${\displaystyle {\frac {1}{(n+a)}}\left\{\cos[\pi W(n+a)]-\operatorname {sinc} [W(n+a)]\right\}}$	${\displaystyle X_{o}(\omega )={\frac {j\omega }{W}}\cdot \operatorname {rect} \left({\omega \over \pi W}\right)e^{ja\omega }}$	real numbers ${\displaystyle W,a}$  with ${\displaystyle 0<W<1}$
${\displaystyle {\begin{cases}{\frac {\pi }{2}}&n=0\\{\frac {(-1)^{n}-1}{\pi n^{2}}}&{\text{ otherwise}}\end{cases}}}$	${\displaystyle X_{o}(\omega )=|\omega |}$
${\displaystyle {\begin{cases}0;&n{\text{ even}}\\{\frac {2}{\pi n}};&n{\text{ odd}}\end{cases}}}$	${\displaystyle X_{o}(\omega )={\begin{cases}j&\omega <0\\0&\omega =0\\-j&\omega >0\end{cases}}}$	Hilbert transform
${\displaystyle {\frac {C(A+B)}{2\pi }}\cdot \operatorname {sinc} \left[{\frac {A-B}{2\pi }}n\right]\cdot \operatorname {sinc} \left[{\frac {A+B}{2\pi }}n\right]}$	${\displaystyle X_{o}(\omega )=}$	real numbers ${\displaystyle A,B}$  complex ${\displaystyle C}$

Properties

This table shows some mathematical operations in the time domain and the corresponding effects in the frequency domain.

• ${\displaystyle *\!}$  is the discrete convolution of two sequences
• ${\displaystyle x[n]^{*}}$  is the complex conjugate of x[n].

Property	Time domain x[n]	Frequency domain ${\displaystyle X_{2\pi }(\omega )}$	Remarks	Reference
Linearity	${\displaystyle a\cdot x[n]+b\cdot y[n]}$	${\displaystyle a\cdot X_{2\pi }(\omega )+b\cdot Y_{2\pi }(\omega )}$	complex numbers ${\displaystyle a,b}$	^[17]^: p.294
Time reversal / Frequency reversal	${\displaystyle x[-n]}$	${\displaystyle X_{2\pi }(-\omega )\!}$		^[17]^: p.297
Time conjugation	${\displaystyle x[n]^{*}}$	${\displaystyle X_{2\pi }(-\omega )^{*}\!}$		^[17]^: p.291
Time reversal & conjugation	${\displaystyle x[-n]^{*}}$	${\displaystyle X_{2\pi }(\omega )^{*}\!}$		^[17]^: p.291
Real part in time	${\displaystyle \Re {(x[n])}}$	${\displaystyle {\frac {1}{2}}(X_{2\pi }(\omega )+X_{2\pi }^{*}(-\omega ))}$		^[17]^: p.291
Imaginary part in time	${\displaystyle \Im {(x[n])}}$	${\displaystyle {\frac {1}{2i}}(X_{2\pi }(\omega )-X_{2\pi }^{*}(-\omega ))}$		^[17]^: p.291
Real part in frequency	${\displaystyle {\frac {1}{2}}(x[n]+x^{*}[-n])}$	${\displaystyle \Re {(X_{2\pi }(\omega ))}}$		^[17]^: p.291
Imaginary part in frequency	${\displaystyle {\frac {1}{2i}}(x[n]-x^{*}[-n])}$	${\displaystyle \Im {(X_{2\pi }(\omega ))}}$		^[17]^: p.291
Shift in time / Modulation in frequency	${\displaystyle x[n-k]}$	${\displaystyle X_{2\pi }(\omega )\cdot e^{-i\omega k}}$	integer k	^[17]^: p.296
Shift in frequency / Modulation in time	${\displaystyle x[n]\cdot e^{ian}\!}$	${\displaystyle X_{2\pi }(\omega -a)\!}$	real number ${\displaystyle a}$	^[17]^: p.300
Decimation	${\displaystyle x[nM]}$	${\displaystyle {\frac {1}{M}}\sum _{m=0}^{M-1}X_{2\pi }\left({\tfrac {\omega -2\pi m}{M}}\right)\!}$   ^[E]	integer ${\displaystyle M}$
Time Expansion	${\displaystyle \scriptstyle {\begin{cases}x[n/M]&n={\text{multiple of M}}\\0&{\text{otherwise}}\end{cases}}}$	${\displaystyle X_{2\pi }(M\omega )\!}$	integer ${\displaystyle M}$	^[1]^: p.172
Derivative in frequency	${\displaystyle {\frac {n}{i}}x[n]\!}$	${\displaystyle {\frac {dX_{2\pi }(\omega )}{d\omega }}\!}$		^[17]^: p.303
Integration in frequency	${\displaystyle \!}$	${\displaystyle \!}$
Differencing in time	${\displaystyle x[n]-x[n-1]\!}$	${\displaystyle \left(1-e^{-i\omega }\right)X_{2\pi }(\omega )\!}$
Summation in time	${\displaystyle \sum _{m=-\infty }^{n}x[m]\!}$	${\displaystyle {\frac {1}{\left(1-e^{-i\omega }\right)}}X_{2\pi }(\omega )+\pi X(0)\sum _{k=-\infty }^{\infty }\delta (\omega -2\pi k)\!}$
Convolution in time / Multiplication in frequency	${\displaystyle x[n]*y[n]\!}$	${\displaystyle X_{2\pi }(\omega )\cdot Y_{2\pi }(\omega )\!}$		^[17]^: p.297
Multiplication in time / Convolution in frequency	${\displaystyle x[n]\cdot y[n]\!}$	${\displaystyle {\frac {1}{2\pi }}\int _{-\pi }^{\pi }X_{2\pi }(\nu )\cdot Y_{2\pi }(\omega -\nu )d\nu \!}$	Periodic convolution	^[17]^: p.302
Cross correlation	${\displaystyle \rho _{xy}[n]=x[-n]^{*}*y[n]\!}$	${\displaystyle R_{xy}(\omega )=X_{2\pi }(\omega )^{*}\cdot Y_{2\pi }(\omega )\!}$
Parseval's theorem	${\displaystyle E_{xy}=\sum _{n=-\infty }^{\infty }{x[n]\cdot y[n]^{*}}\!}$	${\displaystyle E_{xy}={\frac {1}{2\pi }}\int _{-\pi }^{\pi }{X_{2\pi }(\omega )\cdot Y_{2\pi }(\omega )^{*}d\omega }\!}$		^[17]^: p.302

See also

• Least-squares spectral analysis
• Multidimensional transform
• Zak transform

Notes

1. When the dependency on T is unimportant, a common practice is to replace it with ${\displaystyle 1.}$  Then f  has units of (cycles/sample), called normalized frequency.
2. In fact Eq.2 is often justified as follows:^[1]: p.143
   $${\displaystyle {\begin{aligned}{\mathcal {F}}\left\{\sum _{n=-\infty }^{\infty }T\cdot x(nT)\cdot \delta (t-nT)\right\}&={\mathcal {F}}\left\{x(t)\cdot T\sum _{n=-\infty }^{\infty }\delta (t-nT)\right\}\\&=X(f)*{\mathcal {F}}\left\{T\sum _{n=-\infty }^{\infty }\delta (t-nT)\right\}\\&=X(f)*\sum _{k=-\infty }^{\infty }\delta \left(f-{\frac {k}{T}}\right)\\&=\sum _{k=-\infty }^{\infty }X\left(f-{\frac {k}{T}}\right).\end{aligned}}}$$

    
3. WOLA should not be confused with the Overlap-add method of piecewise convolution.
4. WOLA example: File:WOLA channelizer example.png
5. This expression is derived as follows:^[1]: p.168

   ${\displaystyle {\begin{aligned}\sum _{n=-\infty }^{\infty }x(nMT)\ e^{-i\omega n}&={\frac {1}{MT}}\sum _{k=-\infty }^{\infty }X\left({\tfrac {\omega }{2\pi MT}}-{\tfrac {k}{MT}}\right)\\&={\frac {1}{MT}}\sum _{m=0}^{M-1}\quad \sum _{n=-\infty }^{\infty }X\left({\tfrac {\omega }{2\pi MT}}-{\tfrac {m}{MT}}-{\tfrac {n}{T}}\right),\quad {\text{where}}\quad k\rightarrow m+nM\\&={\frac {1}{M}}\sum _{m=0}^{M-1}\quad {\frac {1}{T}}\sum _{n=-\infty }^{\infty }X\left({\tfrac {(\omega -2\pi m)/M}{2\pi T}}-{\tfrac {n}{T}}\right)\\&={\frac {1}{M}}\sum _{m=0}^{M-1}\quad X_{2\pi }\left({\tfrac {\omega -2\pi m}{M}}\right)\end{aligned}}}$ 

Page citations

1. Oppenheim and Schafer,^[1] p 147 (4.20), p 694 (10.1), and Prandoni and Vetterli,^[2] p 255, (9.33), where:  ${\displaystyle x[n]\triangleq x(nT)={\tfrac {1}{T}}x[n],}$  therefore ${\displaystyle X(e^{i\omega })\triangleq {\tfrac {1}{T}}X_{2\pi }(\omega ).}$   Also ${\displaystyle \omega \triangleq 2\pi fT,}$   and  ${\displaystyle X_{c}(i2\pi f)\triangleq X(f).}$ 
2. Oppenheim and Schafer,^[1] p 551 (8.35), and Prandoni and Vetterli,^[2] p 82, (4.43). With definitions:  ${\displaystyle {\tilde {X}}(e^{i\omega })\triangleq {\tfrac {1}{T}}X_{2\pi }(\omega ),}$    ${\displaystyle \omega \triangleq 2\pi fT,}$   ${\displaystyle {\tilde {X}}[k]\triangleq X[k],}$   and  ${\displaystyle \delta \left(2\pi fT-{\tfrac {2\pi k}{N}}\right)\equiv \delta \left(f-{\tfrac {k}{NT}}\right)/(2\pi T),}$  this expression differs from the references by a factor of ${\displaystyle 2\pi }$  because they lost it in going from the 3rd step to the 4th. Specifically, the DTFT of ${\displaystyle e^{-ian}}$  at § Table of discrete-time Fourier transforms has a ${\displaystyle 2\pi }$  factor that the references omitted.
3. Oppenheim and Schafer,^[1] p 60, (2.169), and Prandoni and Vetterli,^[2] p 122, (5.21)

References

1. Oppenheim, Alan V.; Schafer, Ronald W.; Buck, John R. (1999). "4.2, 8.4". Discrete-time signal processing (2nd ed.). Upper Saddle River, N.J.: Prentice Hall. ISBN 0-13-754920-2. samples of the Fourier transform of an aperiodic sequence x[n] can be thought of as DFS coefficients of a periodic sequence obtained through summing periodic replicas of x[n]. 
2. Prandoni, Paolo; Vetterli, Martin (2008). Signal Processing for Communications (PDF) (1 ed.). Boca Raton, FL: CRC Press. pp. 72, 76. ISBN 978-1-4200-7046-0. Retrieved 4 October 2020. the DFS coefficients for the periodized signal are a discrete set of values for its DTFT
3. Rao, R. (2008). Signals and Systems. Prentice-Hall Of India Pvt. Limited. ISBN 9788120338593.
4. "Periodogram power spectral density estimate - MATLAB periodogram".
5. Gumas, Charles Constantine (July 1997). . Personal Engineering & Instrumentation News: 58–64. Archived from the original on 2001-02-10.{{cite journal}}: CS1 maint: bot: original URL status unknown (link)
6. Crochiere, R.E.; Rabiner, L.R. (1983). "7.2". Multirate Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall. pp. 313–326. ISBN 0136051626.
7. Wang, Hong; Lu, Youxin; Wang, Xuegang (16 October 2006). "Channelized Receiver with WOLA Filterbank". 2006 CIE International Conference on Radar. Shanghai, China: IEEE. pp. 1–3. doi:10.1109/ICR.2006.343463. ISBN 0-7803-9582-4. S2CID 42688070.
8. Lyons, Richard G. (June 2008). "DSP Tricks: Building a practical spectrum analyzer". EE Times. Retrieved 2020-02-20.   Note however, that it contains a link labeled weighted overlap-add structure which incorrectly goes to Overlap-add method.
9. Lillington, John (March 2003). (PDF). Dallas: International Signal Processing Conference. p. 4 (fig 7). S2CID 31525301. Archived from the original (PDF) on 2019-03-08. Retrieved 2020-09-06. The "Weight Overlap and Add" or WOLA or its subset the "Polyphase DFT", is becoming more established and is certainly very efficient where large, high quality filter banks are required.
10. Lillington, John. "A Review of Filter Bank Techniques - RF and Digital" (PDF). armms.org. Isle of Wight, UK: Libra Design Associates Ltd. p. 11. Retrieved 2020-09-06. Fortunately, there is a much more elegant solution, as shown in Figure 20 below, known as the Polyphase or WOLA (Weight, Overlap and Add) FFT.
11. Hochgürtel, Stefan (2013). "Efficient implementations of high-resolution wideband FFT-spectrometers and their application to an APEX Galactic Center line survey" (PDF). hss.ulb.uni-bonn.de. Bonn: Rhenish Friedrich Wilhelms University of Bonn. pp. 26–27. Retrieved 2020-09-06. To perform M-fold WOLA for an N-point DFT, M·N real input samples a_j first multiplied by a window function w_j of same size
12. Chennamangalam, Jayanth (2016-10-18). "The Polyphase Filter Bank Technique". CASPER Group. Retrieved 2016-10-30.
13. Dahl, Jason F. (2003-02-06). Time Aliasing Methods of Spectrum Estimation (Ph.D.). Brigham Young University. Retrieved 2016-10-31.
14. Lin, Yuan-Pei; Vaidyanathan, P.P. (June 1998). "A Kaiser Window Approach for the Design of Prototype Filters of Cosine Modulated Filterbanks" (PDF). IEEE Signal Processing Letters. 5 (6): 132–134. Bibcode:1998ISPL....5..132L. doi:10.1109/97.681427. S2CID 18159105. Retrieved 2017-03-16.

January 01, 1970