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#X obj 72 411 mtof;
#X floatatom 72 388 5 0 0 0 - #0-pit -;
#X obj 41 542 output~;
#X obj 41 457 lop~;
#X obj 42 354 noise~;
#X text 124 387 <-- cutoff (pitch units);
#X text 135 434 <-- cutoff (Hertz);
#X floatatom 72 436 5 0 0 0 - - -;
#X text 348 582 updated for Pd version 0.39;
#X text 88 459 low-pass filter;
#X obj 130 535 tabwrite~ H01-graph;
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#X coords 0 1 882 -1 200 140 1;
#X restore 384 386 graph;
#X text 408 528 --- 0.02 sec ---;
#X text 28 30 This and the following patches show how to use filters
in Pd \, starting with the simplest one: the one-pole low-pass filter.
Here we test it with an input of white noise. The lop~ object does
the filtering. Its left inlet takes an audio signal to be filtered
\, and its right inlet takes messages to set its cutoff frequency in
Hertz.;
#X text 26 129 The lop~ object is normalized to pass DC (the lowest
frequency) with a gain of one. Higher frequencies are progressively
more and more attenuated. The lower the cutoff frequency \, the lower
the total power of the filtered noise. If you graph the output you'll
see that the waveform gets smoother (and smaller overall) as the cutoff
frequency is lowered.;
#X text 28 243 At the cutoff frequency the gain is about -3 dB \, and
above that the gain drops a further 6 dB per octave. (Sometimes one
uses the word "rolloff" instead of "cutoff" to emphasize the gradual
way the gain drops off with frequency.);
#X text 108 353 white noise \, test signal;
#X text 185 6 ONE-POLE LOW-PASS FILTER;
#N canvas 0 0 450 300 loadbang 0;
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#X obj 85 59 f \$0;
#X text 18 179 boxes.;
#X text 16 161 This subpatch loads initial values in number;
#X msg 84 83 \; \$1-pit 60;
#X connect 0 0 1 0;
#X connect 1 0 2 0;
#X connect 2 0 5 0;
#X restore 129 582 pd loadbang;
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