pd-ekext 0.1.1-2 / usr / lib / pd / extra / ekext / weightonset-help.pd

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#X text 153 42 threshold (0-10+);
#X text 43 -1 ...get a "highest apparent spectral component" value
from the FFT;
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#X text 56 49 The second value is multipled by the value in the second
inlet (range 0-1) and added to the first value. Then \, the divider
value (in order to work out the weighted average) is also multiplied
by this value \, and added to it.;
#X text 46 8 This object makes a moving average with successively less
important values. The calculation initiates when the object is banged
\, and the first value is the most influential.;
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#X text 78 161 When the object is banged the right outlet sends a list
of all the values that influenced the result since the last bang \,
up to a limit of 1024 values.;
#X obj 477 73 weightonset;
#X text 476 58 V;
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#X text 452 20 floats in;
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#X text 685 338 ^;
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#X text 684 351 |;
#X text 738 338 ^;
#X text 737 342 |;
#X text 737 351 |;
#X text 801 338 ^;
#X text 800 342 |;
#X text 800 351 |;
#X text 671 377 of successive inputs;
#X text 475 89 |;
#X text 475 99 |;
#X text 475 109 |;
#X text 475 119 V;
#X text 452 141 influenced;
#X text 464 130 onset-;
#X text 465 153 value;
#X text 538 89 |;
#X text 538 99 |;
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#X text 538 119 V;
#X text 531 132 data dump;
#X text 537 143 on bang;
#X msg 49 382 5;
#X text 212 439 factor;
#X text 202 415 successive;
#X text 64 101 The influence of successive values is lessened by the
inverse of the factor on the right inlet. So \, a value of 0.2 in the
second inlet means that the second value after the bang is 20% as influential
as the first \, and the third value is 20% of 20% (4%) as influential
as the first.;
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#X text 509 200 influence factor = 0 : 1st float output only;
#X text 510 188 attack length = 0 \,;
#X text 535 166 EXAMPLES:;
#X text 520 226 attack length = 0 \,;
#X text 519 238 influence factor = 1: successive values are;
#X text 232 475 phase;
#X text 232 489 0-100;
#X text 508 252 equally influential to the total \, but the list;
#X text 492 31 successive influence factor (0-1);
#X text 538 58 V;
#X text 538 54 |;
#X text 514 265 grows longer as the numbers are added. Therefore;
#X text 506 278 the output responds less and less to input values.
;
#X text 513 291 This is an accumulating average between bangs.;
#X text 511 313 attack length = 0 \,;
#X text 634 366 (w) = weightings (influence values);
#X text 509 393 the output is onset-weighted e.g. the most influential
values are at the start of the series.;
#X text 699 462 ^;
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#X text 769 462 ^;
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#X text 832 462 ^;
#X text 831 466 |;
#X text 831 475 |;
#X text 680 501 of successive inputs;
#X text 656 490 (w) = weightings (influence values);
#X text 517 437 attack length = 3 \,;
#X text 509 327 influence factor = 0.5: v1 w 1 \, v2 w 0.5 \, v3 w
0.25 \, v4 w 0.125;
#X text 86 200 If the attack phase is greater than 0 \, the values
become successively more influential to the weighted average \, until
the number of values is equal to the attack+1. The next value after
the attack phase is the most influential \, then the influence factor
kicks in and successive values become less influential.;
#X text 557 71 creation args: influence factor \, attack;
#X obj 96 495 weightonset 0.8 0;
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#X text 513 451 influence factor = 0.5: v1 w 0.125 \, v2 w 0.25 \,
v3 w 0.5 \, v4 w 1 \, v5 w 0.5 \, v6 w 0.25...;
#X text 525 43 attack phase (0-100+);
#X obj 664 183 weightonset 0 0;
#X obj 666 222 weightonset 1 0;
#X obj 674 312 weightonset 0.5 0;
#X obj 676 434 weightonset 0.5 3;
#X text 509 517 the output is onset-weighted e.g. the most influential
values are at the start of the series \, after the attack phase in
which the values become successively more influential.;
#X text 99 269 The weightings of values during the attack phase is
the reverse of successive weightings after the attack:;
#X text 226 294 weighting = influence pow(attack - index);
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