#N canvas 615 78 536 572 12;
#X declare -path else;
#X obj 126 189 fexpr~ ($x1[0] + $x1[-1] + $x1[-2] + $x1[-3] + $x1[-4]
+ $x1[-5] + $x1[-6] + $x1[-7]) * 0.125, f 46;
#X obj 292 401 v a;
#X obj 295 356 hsl 128 15 0 0.999 0 0 empty empty empty -2 -8 0 10
-228856 -1 -1 0 1;
#X floatatom 292 377 0 0 0.999 0 - - -;
#X text 106 372 IIR:;
#X text 90 196 FIR:;
#X obj 142 344 noise~;
#X obj 142 373 fexpr~ $x + a * $y;
#X obj 142 411 out~;
#X text 49 153 An example of FIR filter is the average filter:;
#X text 319 402 coefficient;
#X text 51 264 And now as an example of IIR \, the one sample feedback
filter from the last example - the difference is that we're abreviatting
the variables and using a value object to insert in the equation as
the coefficient:;
#X obj 332 24 declare -path else;
#X text 58 68 A filter with only feedforward lines is called "non recursive"
and "Finite Impulse Response" \, or just "FIR". On the other hand \,
a filter with feedback is known as "recursive" or Ininite Impulse Response
(IIR). This is because a feedback can lead to infinite repetitions.
, f 56;
#X text 52 496 Note that if the coefficient is equal to 1 \, then the
filter becomes indeed infinite. Values over 1 explode the filter pretty
quickly!;
#X connect 2 0 3 0;
#X connect 3 0 1 0;
#X connect 6 0 7 0;
#X connect 7 0 8 0;
