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SOURCE-SIGNAL ANALYSIS 561<br />

o<br />

-5<br />

-10<br />

-15<br />

a; -20<br />

~<br />

....... -25<br />

o<br />

::><br />

:s -30<br />

Q.<br />

~ -35<br />

-40<br />

\<br />

f\(\<br />

f\(\<br />

(\f\<br />

-45<br />

-50<br />

-55<br />

-60<br />

o 78 156 234 312<br />

o<br />

468 624 780<br />

FREQUENCY (Hzl<br />

(A)<br />

936<br />

1,092<br />

1,248<br />

-5<br />

-10<br />

-15<br />

~ -20<br />

..... -25<br />

o ::><br />

!::: -30<br />

...J<br />

Q.<br />

~ -35<br />

-40<br />

-45<br />

-50<br />

-55<br />

-60'-----------"'---------'+'-----'+'-----4"''-----'4-''------'---1--''---'-+-'---'---_<br />

° 156 312 468 624 780 936 1,092<br />

FREQUENCY (Hzl<br />

(81<br />

Fig. 16--11. Equivalent half-bandpass filter shapes <strong>of</strong> common windows. (A)<br />

Rectangular window. (B) Triangular window.<br />

Of course, an FFT <strong>of</strong> the window shape will only evaluate the equivalent<br />

BPF at certain discrete frequencies. To get an accurate plot <strong>of</strong> the curve<br />

shape, a very large FFT must be performed with the window occupying a<br />

small portion at the beginning and zeroes occupying the remainder. For<br />

example, to check the response at eight different frequencies between each<br />

null point, an FFT eight times the window length will have to be computed.<br />

In our example, this means a 4,096-poinr FFT. On the other hand, most all

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