Page 43 - Prosig Catalogue 2005

SOFTWARE PRODUCTS
A, B & C WEIGHTING

contributions is the AI value. This may be expressed as shown below. comparison vector for each third octave band. If a measured third octave
noise level is less than j comparison levels in its vector then the added
Contribution = IdealisedSpectrumdB[k] – NoiseLeveldB[k] contribution is (j * 0.01). There are of course 100 comparison levels.
If (Contribution < 0.0) Contribution = 0.0 Figure 2 shows the example third octave background noise level given in
If (Contribution > 30.0 ) Contribution = 30.0 the ANSI specification. This has an overall level of 75.2dB and an ANSI
Articulation Index of 0.547
Contribution = Contribution * WeightingFactor[k]
The $AI_ANSI and $AI_Veh values were calculated for this spectrum and
The contribution is found for each third octave band in the specified several identically shaped spectra adjusted to different overall levels. The Training & Support
frequency range and summed to give the AI value. loudness in Sones was also computed. Results are shown in the table
There is however some confusion as there are three separate approaches below.
for calculating the AI value. One method is the strict ANSI S3.5-1969
scheme, another one is generally known as the vehicle AI value and the Overall dB $AI_ANSI $AI_Veh Loudness
third one as the Room AI value. We distinguish between these as $AI_ % Sones
ANSI, $AI_Veh and $AI_Room. The ANSI method uses third octaves in 45 0.547 99.70 2.93
the bands 200Hz to 5kHz whilst the Vehicle and Room versions add the
6.3kHz band as well. The fundamental difference in the calculations is that 55 0.547 94.21 6.24
the ANSI scheme attempts to take account of the existing overall noise 65 0.547 76.89 12.31
level to adjust the levels of the Idealized Spectrum. The idea here is that if
the background noise level changes then we speak either louder or softer 75 0.547 46.61 23.35
as appropriate. That is it is strictly concerned with speech intelligibility and 85 0.544 18.77 43.27
is not as concerned with the volume or loudness required. The vehicle and 95 0.410 2.75 79.37 Condition Monitoring
room versions of the AI are concerned with assessing sound quality in the
interior environment of the vehicle or room. Thus they use what may be 105 0.204 0 149.65
described as a fixed target speech spectrum. In consequence the overall
level as well as the spectrum shape affect the metric. By convention the Note The $AI_ANSI value is shown as an index from zero to unity but that
$AI_ANSI and $AI_Room values are usually given as an index from zero $AI_Veh is shown as a percentage.
to unity but the $AI_Veh is usually given as a percentage. The $AI_Veh From the table it is clear that the ANSI AI is sensibly independent of the
and $AI_Room give quite similar values. Figure 1 above shows the ANSI overall level until the anechoic factors take effect at high overall levels.
Ideal Speech spectrum, the fixed ‘target’ spectrum for $AI_Veh and a The Vehicle AI however with its fixed target does vary with overall level. It
raised version of the ANSI spectrum whose overall matches that of the has essentially an inverse relationship of some form to loudness.
vehicle target spectrum.
Both AI calculation methods are valid for the purposes for which they
The differences in the two principal spectra are obvious. However by were designed. The ANSI version tests speech intelligibility, the vehicle
comparing the ANSI ‘raised’ spectrum to the vehicle ‘target’ spectrum, it and room versions test what may be called normal level speech quality.
is clear that the vehicle target spectrum is more accommodating at the Software
higher frequencies but less tolerant at the lower frequencies.
The ANSI method uses 65dB as the reference level to adjust for the A, B and C Weighting
overall level of the background noise level. If the background noise has an
overall level of P dB, then (P –65) dB is added to each idealized spectrum The DATS analysis function WEIGHT provides the ability to apply A, B,
third octave level. That is to a large extent $AI_ANSI is independent of the C or D weighting to any frequency spectra. The input may be an FFT,
overall level. This is not the case for $AI_Veh which uses a fixed idealized an auto-spectrum or a cross spectrum and may be in real, complex or
speech spectrum level. modulus & phase form.
The ANSI scheme also has an absolute ‘maximum tolerable level’ and a Some devices, particularly digital tape recorders, apply A-weighting to
‘threshold level’ for each third octave band. Thus if any adjusted level all their data in order to achieve acceptable data compression. This is
is above or below these, then the corresponding limit value is used in fine unless you want to analyze the unweighted data or apply a different
the adjusted spectrum. There is also another aspect in the $AI_ANSI weighting factor. Using DATS it is a simple task to instruct the WEIGHT
calculation for high overall level signals. This is an anechoic correction
which basically reduces the idealized speech spectrum so that the $AI_ Hardware
ANSI value falls with very loud background noise levels. The $AI_Veh and
$AI_Room calculations do not have these factors.

System Packages

Figure 2: Third octave background noise level

The final difference between the three approaches is that each has
different weighting values. All the sets of weighting values are biased
towards the 1.6 and 2kHz bands with the $AI_ANSI being slightly flatter.
Actually the $AI_Room calculation method is slightly different as it uses a Fig. 1 : Example of A, B & C weighting

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