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C H A P T E R
6 :
Note:
Never compare:
•
MOS values obtained from PESQ measurements to those
obtained from PSQM measurements.
•
MOS values obtained from PSQM implementations from
different manufacturers. They may and usually do differ
significantly (of course not between different OPERA
systems....).
6.5 PSQM Measurement
6.5.1 Fundamentals of the PSQM Measurement Algorithm
The algorithm to calculate the perceptual speech quality measure (PSQM) was
introduced by Beerends in 1993 [BEER94]. This development by KPN Research
represents an adapted version of the more general perceptual audio quality
measure (PAQM) [BEER92], optimized for telephony speech signals. This is due
to the observation that the psycho-acoustic effects known from masking
experiments differ significantly, when comparing the perception of speech and
music signals. One reason might be that the human brain possibly recalls the
reference sound of familiar voices more accurately from the daily life
experience, compared to music sounds. Up to now, no single homogeneous
approach has been presented that would allow for high correlation with both,
speech, and music signals without adapting algorithm parameters [BEER95].
Figure 6.3 depicts a detailed block diagram to calculate PSQM. In the first
step, the time domain representations of both input signals, x and y are
transformed to the frequency domain. This transformation is accomplished by
selecting blocks of the input samples that are input to an FFT. A Hann window
is applied. The (linear) frequency scale is transformed to a pitch scale
("frequency warping"). The pitch modelling is also often referred to as "Bark
transformation". Both, the reference, and the test signal are then filtered with the
transfer characteristics of the receiving device (e.g. handset, loudspeaker, or
headphones). A "Hoth noise" signal is added to simulate the background noise
present in a typical office environment. The objective is to take into account the
masking effects of real world environment noise, to properly model a masked
threshold. The subsequent process of "intensity warping" leads to a
representation of a compressed loudness as a function of pitch and time. By
subtracting the two signal representations, an estimate of the audible error is
derived. The difference signal is - of course - still a function of pitch and time.
T E L E P H O N Y
B A N D
T E S T I N G
121
V O I C E
Q U A L I T Y
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