68 lines
1.2 KiB
Text
68 lines
1.2 KiB
Text
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class:: MantissaMask
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summary:: Reduce precision.
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categories:: UGens>Filters>Nonlinear
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Description::
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Masks off bits in the mantissa of the floating point sample value.
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This introduces a quantization noise, but is less severe than linearly
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quantizing the signal.
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classmethods::
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method::ar, kr
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argument::in
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The input signal.
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argument::bits
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The number of mantissa bits to preserve. A number from 0 to 23.
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argument::mul
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Output will be multiplied by this value.
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argument::add
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This value will be added to the output.
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Examples::
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code::
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// preserve only 3 bits of mantissa.
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{ MantissaMask.ar(SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4), 3) }.play
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// the original
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{ SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4) }.play
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// the difference.
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(
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{
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var in;
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in = SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4);
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Out.ar(0, in - MantissaMask.ar(in, 3));
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}.play
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)
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// preserve 7 bits of mantissa.
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// This makes the lower 16 bits of the floating point number become zero.
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{ MantissaMask.ar(SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4), 7) }.play
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// the original
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{ SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4) }.play
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// the difference.
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(
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{
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var in;
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in = SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4);
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Out.ar(0, in - MantissaMask.ar(in, 7));
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}.play
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)
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::
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