Hamming Window

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Spectral Audio Signal Processing

Generalized Hamming Window Family

Matlab for the Hann Window

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A filter in the audio signal processing context is any operation that accepts a signal as an input and produces a signal as an output. Most practical audio filters are linear and time invariant, in which case they can be characterized by their impulse response or their frequency response. — Click for https://ccrma.stanford.edu/~jos/filters/What_Filter.html

Noise (in signal processing) is a random signal. In the language of statistical signal processing, a noise signal is typically modeled as 'stochastic process', which is in turn defined as a sequence of random variables. — Click for https://ccrma.stanford.edu/~jos/sasp/What_Noise.html

A signal is typically a real-valued function of time. A discrete-time signal is typically a real-valued function of discrete time, and is therefore a time-ordered sequence of real numbers. — Click for http://ccrma.stanford.edu/~jos/filters/Definition_Signal.html

The sampling rate of a discrete-time signal is defined as the number of samples per second. Its units are thus in Hertz (Hz). Shannon's Sampling Theorem states that the original continuous-time signal can be recovered exactly from the samples if and only if the sampling rate is higher than twice the highest frequency present in the original signal. Any higher frequencies will alias to frequencies below half the sampling rate. — Click for https://ccrma.stanford.edu/~jos/mdft/Sampling_Theory.html

Spectrum analysis of sound is analogous to decomposing white light into its component colors by means of a prism — Click for https://ccrma.stanford.edu/~jos/mdft/Example_Applications_DFT.html

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The amplitude envelope, or relatively slowly-changing outline of a sound waveform, makes for a useful first approximation of the instantaneous loudness of the sound. — Click for http://en.wikipedia.org/wiki/ADSR_envelope

The main lobe in the Fourier transform of a window function, such as the Chebyshev window, is the central local maximum in the transform magnitude (response about dc). Outside of the main lobe are usually side lobes (ripples in the magnitude spectrum). — Click for https://ccrma.stanford.edu/~jos/sasp/Rectangular_Window_Side_Lobes.html

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A periodic signal is a signal that forever repeats itself. — Click for http://en.wikibooks.org/wiki/Signals_and_Systems/Periodic_Signals

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The Discrete Time Fourier Transform (DTFT) is the appropriate Fourier transform for discrete-time signals of arbitrary length. It can be obtained as the limit of a Discrete Fourier Transform (DFT) as its length goes to infinity. — Click for https://ccrma.stanford.edu/~jos/mdft/Discrete_Time_Fourier_Transform.html

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A side lobe in the Fourier transform of a window function, such as the Chebyshev window, is any local maximum in the transform magnitude outside of the main lobe (response about dc). — Click for https://ccrma.stanford.edu/~jos/sasp/Rectangular_Window_Side_Lobes.html

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Spectral Audio Signal Processing

Generalized Hamming Window Family

Matlab for the Hann Window

Matlab for the Hamming Window

Hamming Window

The Hamming window is determined by choosing $\alpha$ in (3.17) (with $\beta\isdeftext (1-\alpha)/2$ ) to cancel the largest side lobe [101].^4.4 Doing this results in the values

$\begin{eqnarray*} \alpha &=& \frac{25}{46} \approx 0.54\\ [5pt] \beta &=& \frac{1-\alpha}{2} \approx 0.23. \end{eqnarray*}$

The peak side-lobe level is approximately dB for the Hamming window [101].^4.5 It happens that this choice is very close to that which minimizes peak side-lobe level (down to dB--the lowest possible within the generalized Hamming family) [196]:

$\displaystyle \alpha = 0.53836\ldots$

(4.19)

Since rounding the optimal $\alpha$ to two significant digits gives

, the Hamming window can be considered the ``Chebyshev Generalized Hamming Window'' (approximately). Chebyshev-type designs normally exhibit equiripple error behavior, because the worst-case error (side-lobe level in this case) is minimized. However, within the generalized Hamming family, the asymptotic spectral roll-off is constrained to be at least

dB per octave due to the form (3.17) of all windows in the family. We'll discuss the true Chebyshev window in §3.10 below; we'll see that it is not monotonic from its midpoint to an endpoint, and that it is in fact impulsive at its endpoints. (To peek ahead at a Chebyshev window and transform, see Fig.3.31.) Generalized Hamming windows can have a step discontinuity at their endpoints, but no impulsive points.

**Figure 3.10:** A Hamming window and its transform.
$\includegraphics[width=\twidth]{eps/hammingWindow}$

The Hamming window and its DTFT magnitude are shown in Fig.3.10. Like the Hann window, the Hamming window is also one period of a raised cosine. However, the cosine is raised so high that its negative peaks are above zero, and the window has a discontinuity in amplitude at its endpoints (stepping discontinuously from 0.08 to 0). This makes the side-lobe roll-off rate very slow (asymptotically dB/octave). On the other hand, the worst-case side lobe plummets to dB,^4.6which is the purpose of the Hamming window. This is 10 dB better than the Hann case of Fig.3.9 and 28 dB better than the rectangular window. The main lobe is approximately $4\Omega_M$ wide, as is the case for all members of the generalized Hamming family ( $\beta\neq 0$ ).

Due to the step discontinuity at the window boundaries, we expect a spectral envelope which is an aliased version of a dB per octave (i.e., a $1/\omega$ roll-off is converted to a ``cosecant roll-off'' by aliasing, as derived in §3.1 and illustrated in Fig.3.6). However, for the Hamming window, the side-lobes nearest the main lobe have been strongly shaped by the optimization. As a result, the nearly dB per octave roll-off occurs only over an interior interval of the spectrum, well between the main lobe and half the sampling rate. This is easier to see for a larger , as shown in Fig.3.11, since then the optimized side-lobes nearest the main lobe occupy a smaller frequency interval about the main lobe.

**Figure 3.11:** A longer Hamming window and its transform.
$\includegraphics[width=\twidth]{eps/hammingWindowLong}$

Since the Hamming window side-lobe level is more than 40 dB down, it is often a good choice for ``1% accurate systems,'' such as 8-bit audio signal processing systems. This is because there is rarely any reason to require the window side lobes to lie far below the signal quantization noise floor. The Hamming window has been extensively used in telephone communications signal processing wherein 8-bit CODECs were standard for many decades (albeit $\mu$ -law encoded). For higher quality audio signal processing, higher quality windows may be required, particularly when those windows act as lowpass filters (as developed in Chapter 9).

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``Spectral Audio Signal Processing'', by Julius O. Smith III, W3K Publishing, 2011, ISBN 978-0-9745607-3-1.
Copyright © 2022-02-28 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA), Stanford University

Hamming Window

``Spectral Audio Signal Processing'', by Julius O. Smith III, W3K Publishing, 2011, ISBN 978-0-9745607-3-1. Copyright © 2022-02-28 by Julius O. Smith III Center for Computer Research in Music and Acoustics (CCRMA), Stanford University

``Spectral Audio Signal Processing'', by Julius O. Smith III, W3K Publishing, 2011, ISBN 978-0-9745607-3-1.
Copyright © 2022-02-28 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA), Stanford University