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Prism Sound Orpheus
occurring, this is known as 'rectangular probability distribution function' (RPDF) dither, which in fact
does not produce perfect linearization. We actually use 'triangular probability distribution function'
(TPDF) dither, which is like throwing two dice with a resultant increase in the probability of medium
sized numbers – totals of two and twelve occur much less often than seven.
Noise shaping
It is possible to reduce the subjective effect of the added dither noise by either using spectrally
weighted (''blue'') dither noise, which is quieter in the more sensitive registers of the ear, or by an
even more effective technique called 'noise shaping'.
Noise shaping is just like conventional dithering, except that the error signal generated when the
unwanted low-order bits are discarded is filtered and subtracted from the input signal. You can't get
something for nothing – the error cannot be simply cancelled out, because we already know that the
output hasn't got enough bits to precisely represent the input. But by choosing an appropriate shape
for the error filter, we can force the dither noise / error signal to adopt the desired shape in the
frequency domain – we usually choose a shape which tracks the low-field perception threshold of the
human ear against frequency. As can be seen from the plots below, this has the effect of actually
lowering the noise floor in the more sensitive frequency bands when compared to the flat dither case.
The theory of noise shaping has been around for a long time – certainly since well before DSP in
real-time was feasible for audio signals. It has applications in many signal processing and data
conversion applications outside audio. It has been well researched, and is not in the least bit
mysterious. 'Proprietary' word-length reduction algorithms are generally conventional noise shapers.
Assuming that the basic implementation and dither levels are correct, the only significant freedoms
available to the designer are to choose the actual shape of the noise floor, and to decide how to adapt
this (if at all) to different sample rates.
Prism Sound SNS (Super Noise Shaping)
Orpheus provides a comprehensive choice of dithering and noise-shaping processes. These
comprise 'flat' dithering, plus a selection of four Prism Sound 'SNS' ('Super Noise Shaping')
algorithms. All produce high-quality 16 bit output: the choice of which one to use is purely subjective.
The four SNS algorithms are designated SNS1 to SNS4, in increasing order of the degree of shaping.
The spectra of the four SNS algorithms are shown below. Note that, unlike some noise shaping
algorithms, SNS spectra are adjusted automatically to provide optimum subjective advantage at each
different sample rate. The spectra are shown below for 16-bit output, at 44.1kHz, 48kHz and 96kHz
sample rates.
SNS1 provides the smallest subjective noise advantage, but only applies limited
noise-lift at quite high frequencies. In many applications, particularly those where
the program material is already quite noisy, this type of shaper is preferred.
SNS2 is a happy medium. It provides a good amount of subjective lowering of the
noise floor, but with addition of only moderate amounts of high-frequency noise. It
also has the advantage that the noise floor remains subjectively white, even when
artificially amplified. In the fifteen years since Prism Sound first developed the four
SNS curves, SNS2 has been the most widely preferred.
SNS3 and SNS4 are 'optimal' shaper designs – their shaping is quite extreme in
order to get the maximum theoretical subjective improvement in noise
performance based on an average human low-field sensitivity curve. This results
in the addition of larger amounts of high-frequency noise. These shapers are only
really useful if the original recording has a very low noise floor.
It is difficult to assess the difference in sound between different noise shapers for any given program
material, since their effects are at very low amplitudes (the 0dB line on the plots below represents flat
dither with an rms noise amplitude of about –93.4dBFS). It is tempting to audition noise shapers by
using a low signal level and boosting the shaper output by tens of dBs in the digital domain prior to
monitoring. Using this method it is easy to hear that the noise floor of more extreme shapers is
© 2008 Prism Media Products Ltd
Operation Manual
Revision 1.05
1.46
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