Noise Shaping; Wordlength And The Ada-8 - Prism Sound ADA-8 Operation Manual

Prism Sound ADA-8 Multi-channel A/D D/A Converter

7.3.2 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, 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' wordlength 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.

7.3.3 Wordlength and the ADA-8

The ADA-8 provides a comprehensive choice of processes to optimally generate a reduced
output wordlength from an analogue or digital source. It also includes encoding processes
which allow extended wordlengths to be recorded on or transmitted via shorter wordlength
equipment, as described in section 7.4.
These comprise 'flat' dithering, plus a selection of four Prism Sound
'SNS' ('Super Noise Shaping') algorithms. 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 and wordlength. The spectra are shown below for 16-bit
output, at 44.1kHz, 48kHz and 96kHz sample rates only.
SNS1 provides only a very small 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 very often 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.
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.
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
© Prism Media Products Limited, 2002
Operation Manual - Revision 1.00
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