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