Probalistic Noise UGens

Extension

LFBrownNoise0.kr(freq, dev, mul, add)... (also LFBrownNoise1, LFBrownNoise2)

0 < dev <= 2. higher values mean bigger steps.

TBetaRand.kr(lo, hi, prob1, prob2, trig)...

prob1 < prob2 means lo is more likely. and vice versa. prob1 & prob2 < 1 leans towards lo and hi values. prob1 & prob2 = 1 is linear random. > 1 tends towards guassian distribution.

TBrownRand.kr(lo, hi, dev, trig)...

0 < dev <= 2. higher values mean bigger steps.

TGaussRand.kr(lo, hi, trig)...

gaussian distribution between lo and hi.

GaussTrig.kr(freq, dev)...

emits a trigger around freq. 0.0 <= dev < 1. 0.0 is like Impulse, higher values introduce more randomness.( { Pan2.ar( SinOsc.ar( Lag.ar( LinExp.ar( TBrownRand.ar(0, 1, 1.0, Dust.ar(8)), 0, 1, 120, 6000 ), 0.02 ), mul: LFBrownNoise2.kr(6, 1.0, 0.4, 0.5) ).squared * Decay2.kr(GaussTrig.kr(8.0, 0.2), 0.02, 0.2), Lag.kr( TBetaRand.kr(-1.0, 1.0, Line.kr(4, 0.1, 5), Line.kr(2, 0.1, 5), Impulse.kr(8.0)), 0.2) ) }.play )