Convolution as a Filtering Operation
In a convolution of two signals
, where both
and
are signals of length
(real or complex), we may interpret either
or
as a filter that operates on the other signal
which is in turn interpreted as the filter's ``input signal''.7.5 Let
denote a length
signal that is interpreted
as a filter. Then given any input signal
, the filter output
signal
may be defined as the cyclic convolution of
and
:







![$ \delta\isdeftext [1,0,\ldots,0]\in{\bf R}^N$](http://www.dsprelated.com/josimages_new/mdft/img1169.png)
![$\displaystyle \delta(n) = \left\{\begin{array}{ll}
1, & n=0\;\mbox{(mod $N$)} \\ [5pt]
0, & n\ne 0\;\mbox{(mod $N$)}. \\
\end{array} \right.
$](http://www.dsprelated.com/josimages_new/mdft/img1170.png)







![$\displaystyle \delta(n) \isdef \left\{\begin{array}{ll}
1, & n=0 \\ [5pt]
0, & n\ne 0 \\
\end{array} \right.
$](http://www.dsprelated.com/josimages_new/mdft/img1172.png)
As discussed below (§7.2.7), one may embed acyclic convolution within a larger cyclic convolution. In this way, real-world systems may be simulated using fast DFT convolutions (see Appendix A for more on fast convolution algorithms).
Note that only linear, time-invariant (LTI) filters can be completely represented by their impulse response (the filter output in response to an impulse at time 0). The convolution representation of LTI digital filters is fully discussed in Book II [68] of the music signal processing book series (in which this is Book I).
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Convolution Example 1: Smoothing a Rectangular Pulse
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Commutativity of Convolution