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Main-Lobe Bandwidth

We saw in the previous section that our ability to resolve two closely spaced sinusoids is determined by the main-lobe width of the window transform we are using. We will now study this relationship in more detail.

For starters, let's define main-lobe width very simply (and somewhat crudely) as the distance between the first zero-crossings on either side of the main lobe, as shown in Fig.1.16 for a rectangular-window transform. Let $ B_w$ denote this width in Hz. In normalized radian frequency units, as used in the frequency axis of Fig.1.16, $ B_w$ Hz translates to $ 2\pi B_w / f_s$ radians per sample, where $ f_s$ denotes the sampling rate in Hz.

Figure 1.16: Window transform with main lobe width marked.
\begin{figure}\input fig/rectWinMLM.pstex_t
\end{figure}

For the length-$ M$ unit-amplitude rectangular window defined in §1.5, the DTFT is given analytically by

$\displaystyle W_R(\omega) = M\cdot\hbox{asinc}_M(\omega) \isdef \frac{ \sin \le...
...in(\omega T/2)} = \frac{ \sin \left( M\pi f T \right)}{\sin(\pi f T)}, \protect$ (2.7)

where $ f$ is frequency in Hz, and $ T$ is the sampling interval in seconds ($ T=1/f_s$). The main lobe of the rectangular-window transform is thus ``two sidelobes wide,'' or

$\displaystyle \zbox {B_w = 2\frac{f_s}{M}}\quad \hbox{(\hbox{Hz})}
$

as can be seen in Fig.1.16.

Recall from §1.5.1 that the side-lobe width in a rectangular-window transform ($ f_s/M$ Hz) is given in radians per sample by

$\displaystyle \zbox {\Omega_M \isdef \frac{2\pi}{M}.}
$

As Fig.1.16 illustrates, the rectangular-window transform main-lobe width is $ 2\Omega_M$ radians per sample (two side-lobe widths). Table 1.1 lists the main-lobe widths for a variety of window types (which are defined and discussed further in Chapter 3).


Table 1.1: Main-lobe bandwidth for various windows.
Window Type Main-Lobe Width $ K\Omega_M$ (rad/sample)
Rectangular $ 2\Omega_M$
Hamming $ 4\Omega_M$
Hann $ 4\Omega_M$
Generalized Hamming $ 4\Omega_M$
Blackman $ 6\Omega_M$
$ L$-term Blackman-Harris $ 2L\Omega_M$
Kaiser depends on $ \beta $
Chebyshev depends on ripple spec




Subsections

Order a Hardcopy of Spectral Audio Signal Processing

Previous: One Sine and One Cosine ``Phase Quadrature'' Case
Next: Other Definitions of Main Lobe Width

written by Julius Orion Smith III
Julius Smith's background is in electrical engineering (BS Rice 1975, PhD Stanford 1983). He is presently Professor of Music and Associate Professor (by courtesy) of Electrical Engineering at Stanford's Center for Computer Research in Music and Acoustics (CCRMA), teaching courses and pursuing research related to signal processing applied to music and audio systems. See http://ccrma.stanford.edu/~jos/ for details.


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