Hann function

Top: 16 sample DFT-even Hann window. Bottom: Its discrete-time Fourier transform (DTFT) and the 3 non-zero values of its discrete Fourier transform (DFT).

The Fourier transform of ${\displaystyle w_{0}(x)}$ is given by:

${\displaystyle W_{0}(f)={\tfrac {1}{2}}{\frac {\sin(\pi Lf)}{\pi f(1-L^{2}f^{2})}}}$   [lower-alpha 1]

An equivalent expression is found from the formulation as a linear combination of modulated rectangular windows:

${\displaystyle \mathrm {rect} (x/L)\quad {\stackrel {\text{Fourier transform}}{\longleftrightarrow }}\quad L\cdot \mathrm {sinc} (Lf)={\frac {\sin(\pi Lf)}{\pi f}}.}$

Using Euler's formula to expand the cosine term, we can write:

${\displaystyle w_{0}(x)={\tfrac {1}{2}}\mathrm {rect} (x/L)+{\tfrac {1}{4}}e^{i2\pi x/L}\mathrm {rect} (x/L)+{\tfrac {1}{4}}e^{-i2\pi x/L}\mathrm {rect} (x/L),}$

whose Fourier transform is just:

${\displaystyle W_{0}(f)={\tfrac {1}{2}}{\frac {\sin(\pi Lf)}{\pi f}}+{\tfrac {1}{4}}{\frac {\sin(\pi L(f-1/L))}{\pi (f-1/L)}}+{\tfrac {1}{4}}{\frac {\sin(\pi L(f+1/L))}{\pi (f+1/L)}}.}$

The Discrete-time Fourier transform (DTFT) of the N+1 length, time-shifted sequence is defined by a Fourier series, which also has a 3-term equivalent that is derived similarly to the Fourier transform derivation:

${\displaystyle {\begin{aligned}{\mathcal {F}}\{w[n]\}&\triangleq \sum _{n=0}^{N}w[n]\cdot e^{-i2\pi fn}\\&=e^{-i\pi fN}\left[{\tfrac {1}{2}}{\frac {\sin(\pi (N+1)f)}{\sin(\pi f)}}+{\tfrac {1}{4}}{\frac {\sin(\pi (N+1)(f-{\tfrac {1}{N}}))}{\sin(\pi (f-{\tfrac {1}{N}}))}}+{\tfrac {1}{4}}{\frac {\sin(\pi (N+1)(f+{\tfrac {1}{N}}))}{\sin(\pi (f+{\tfrac {1}{N}}))}}\right].\end{aligned}}}$

For even values of N, the truncated sequence ${\displaystyle \{w[n],\ 0\leq n\leq N-1\}}$ is a DFT-even (aka periodic) Hann window. Since the truncated sample has value zero, it is clear from the Fourier series definition that the DTFTs are equivalent. However, the approach followed above results in a significantly different-looking, but equivalent, 3-term expression:

${\displaystyle {\mathcal {F}}\{w[n]\}=e^{-i\pi f(N-1)}\left[{\tfrac {1}{2}}{\frac {\sin(\pi Nf)}{\sin(\pi f)}}+{\tfrac {1}{4}}e^{-i\pi /N}{\frac {\sin(\pi N(f-{\tfrac {1}{N}}))}{\sin(\pi (f-{\tfrac {1}{N}}))}}+{\tfrac {1}{4}}e^{i\pi /N}{\frac {\sin(\pi N(f+{\tfrac {1}{N}}))}{\sin(\pi (f+{\tfrac {1}{N}}))}}\right].}$

An N-length DFT of the window function samples the DTFT at frequencies ${\displaystyle f=k/N,}$ for integer values of ${\displaystyle k.}$ From the expression immediately above, it is easy to see that only 3 of the N DFT coefficients are non-zero. And from the other expression, it is apparent that all are real-valued. These properties are appealing for real-time applications that require both windowed and non-windowed (rectangularly windowed) transforms, because the windowed transforms can be efficiently derived from the non-windowed transforms by convolution.[5][lower-alpha 2][lower-alpha 3]

The function is named in honour of von Hann, who used the three-term weighted average smoothing technique on meteorological data.[6][3] However, the erroneous[2] "Hanning" function is also heard of on occasion, derived from the paper in which it was named, where the term "hanning a signal" was used to mean applying the Hann window to it.[7][8] The confusion arose from the similar Hamming function, named after Richard Hamming.

• Window function
• Apodization
• Raised cosine distribution
• Raised-cosine filter

• Hann function at MathWorld