This paper describes how one can assess voice quality in networks by specific waveform analysis non-intrusively. Sevana presents its approach to passive voice quality check based on its research and technology. In the end of the article one can find list of metrics to check when monitoring call quality in VoIP networks.

1. Voice Quality Impairments Detection
Introduction
The purpose of this document is to define a vocabulary that can be used to discuss
symptoms of voice quality problems detection.
This document is intended to be a living resource in that the detection of symptoms listed are
expected to be revised as new problems arise and additional information becomes available.
Signal-to-noise ratio
Signal-to-noise ratio (often abbreviated SNR or S/N) is a measure used in science and engineering that compares
the level of a desired signal to the level of background noise. It is defined as the ratio of signal power to the
noise power. A ratio higher than 1:1 indicates more signal than noise. While SNR is commonly quoted for
electrical signals, it can be applied to any form of signal (such as isotope levels in an ice core or biochemical
signaling between cells).
The signal-to-noise ratio, the bandwidth, and the channel capacity of a communication channel are connected by
the Shannon–Hartley theorem.
Signal-to-noise ratio is sometimes used informally to refer to the ratio of useful information to false or irrelevant
data in a conversation or exchange. For example, in online discussion forums and other online communities,
off-topic posts and spam are regarded as "noise" that interferes with the "signal" of appropriate discussion.
Telecommunication systems strive to increase the ratio of signal level to noise level in order to effectively
transmit data. In practice, if the transmitted signal falls below the level of the noise (often designated as the
noise floor) in the system, data can no longer be decoded at the receiver. Noise in telecommunication systems is
a product of both internal and external sources to the system
We recommend SNR to be not lower than 25dB in speech signal.
Absolute silence
This type of impairment relates to silence between speech whenone cannot recognize whether the
other person is still there because there is no sound on the line.
A common cause for this problem is Voice Activity Detection (VAD) without comfort noise. In order to
experience this symptom, usually the background noise is loud enough for the silence insertion to be
noticeable but soft enough so that VAD is engaged.
Silence appearing during a phone call is considered an artifact that is associated with connection loss.
Therefore one can set energy threshold on a frame and when it goes below threshold value one starts
Copyright © Sevana, 2013
Sevana Oy
Agricolankatu 11
00530 Helsinki
Finland
Phone: +358 9 2316 4165
Sevana Oü
Rohtlaane 12
76911 Huuru kula
Estonia (Harjumaa)
Phone: +372 53485178

2. calculating duration of the silent fragment of the signal. If one receives a “loud” frame then the
counter for silence is reset to zero. If counter value becomes greater than f.e. 1 second then we can
notify about detecting absolute silence impairment and quality loss due to silent fragments..
Loudness
This impairment is related to too loud or too silent calls. In case signal energy changes significantly
one may consider the usual call quality has been degraded. Most recent version of the library works
together with VAD to detect too loud call fragments by calculating average energy values from active
signal fragments (frames), and when the average exceed predefined threshold this indicates that the
signal is too loud.
Amplitude clipping
Amplitude clipping impairment or the so called “buzziness” is related to the fact if the signal amplitude
is too high at some point along the analog voice path, when the voice signal is converted to a digital
form amplitude clipping can occur. Users report that speech may seem excessively loud and potentially
"buzzy" or "fuzzy". One can find a sample of amplitude clipped audio at this link:
http://www.voiptroubleshooter.com/sound_files/amplitude_clipping.wav
In case amount of clipped samples is higher than 2% the audio quality gets considerably lower:
1) To take integral result over a frame one must check dClpLevel and dClpLevelWide. We may consider
single clipped frames as non-critical impairments for overall quality, because it may be due to energy
normalization only. However, if at the same time we have clipped sequences of samples then we face
real clipping impairment. We must alert if dClpLevel > 2% and dClpLevelWide > 0. One can also set a
threshold for dClpLevelWide, f.e. 5% from dClpLevel.
2) For real-time monitoring one should check dFrameClpLevel&dFrameClpLevelWide and
dFlyClpLevel&dFlyClpLevelWide. If we have clipping on a single frame and there are no clipped samples
to the left and to the right from it, this impairment we may identify as a click. Temporary quality
degradation is characterized by clipping in longer parts of the signal. These parts one may
characterize as significant increase of the input signal loudness.
Clicking
Clicking impairment is related to a short time period energy increase - click. If clicks appear more often
than in 3-5 seconds then we have audio quality degradation, what should cause a clicking alert.
Stuck
Stuck means appearance of a relatively constant amplitude level of the signal. Stuck signal one
percepts as absolute silence, what is not typical for speech. Depending on energy change it may also
be percepted as a click. We recommend to set the same threshold for Stuck as for Clicking: not more
than 1 stuck impairment during 3-5 seconds. However, if stuck duration is more than 10% of the whole
audio this is also a signal of significant quality degradation with stuck impairment.
VAD clipping
This impairment detects incorrect work of Voice Activity Detector (VAD). Detector finds edges of
active and inactive fragments of the signal considering VAD worked too late (in the beginning of the
speech) or too early (in the end of the speech).
Copyright © Sevana, 2013
Sevana Oy
Agricolankatu 11
00530 Helsinki
Finland
Phone: +358 9 2316 4165
Sevana Oü
Rohtlaane 12
76911 Huuru kula
Estonia (Harjumaa)
Phone: +372 53485178

3. Let us calculate number of changes of VAD (i.e. voice/no voice) and consider that number is X, then
the following formular
100 * dNumClpFrames/X
calculates a metric, which should not exceed 10% for acceptable speech quality.
Echo
Signal reflection (echo) occurs when a signal is transmitted along a transmission medium, such as a
copper cable or an optical fiber. Some of the signal power may be reflected back to its origin rather
than being carried all the way along the cable to the far end. This happens because imperfections in
the cable cause impedance mismatches and non-linear changes in the cable characteristics. These
abrupt changes in characteristics cause some of the transmitted signal to be reflected. The ratio of
energy bounced back depends on the impedance mismatch. Mathematically, it is defined using the
reflection coefficient.
In telecommunications, the reflection coefficient is the ratio of the amplitude of the reflected wave to
the amplitude of the incident wave. In particular, at a discontinuity in a transmission line, it is the
complex ratio of the electric field strength of the reflected wave (
) to that of the incident wave (
). This is typically represented with a
(capital gamma) and can be written as:
The reflection coefficient may also be established using other field or circuit quantities.
The reflection coefficient can be given by the equations below, where
the source,
is the impedance toward
is the impedance toward the load:
Notice that a negative reflection coefficient means that the reflected wave receives a 180°, or
phase shift.
,
The absolute magnitude (designated by vertical bars) of the reflection coefficient can be calculated
from the standing wave ratio, SWR:
The reflection coefficient range is from -1 to +1
There are two algorithms to detect echo implemented: correlation based and echo compensation
based. Selecting one of them is possible during library compilation.
In case of echo compensation based algorithm one should compare echo energy versus signal energy
and if echo energy is more than 20% from the signal energy then we detect echo presence in the
speech signal. One can also check VAD and compare energy values only when VAD is active.
Copyright © Sevana, 2013
Sevana Oy
Agricolankatu 11
00530 Helsinki
Finland
Phone: +358 9 2316 4165
Sevana Oü
Rohtlaane 12
76911 Huuru kula
Estonia (Harjumaa)
Phone: +372 53485178

4. In case of correlation based algorithm one should consider similarity. We can say echo is present if
correlation is higher than 0.7 and at the same time check signal energy level: in case it’s low then
echo is not present (false positive). One can also consider VAD instead of energy.
Appendix 1: Audio compatibility
We understand acceptable audio quality level if the following conditions are applied to the analyzed
audio:
●
●
●
●
●
●
Average loudness varies from -30dB to 0dB
Number of clipped audio samples does not exceed 2%
SNR is at least 20dB for regular calls and at least 24dB in case of using a loudspeaker
Non-speech signal presence is strictly prohibited
Speech tempo is maximum of 250%
Presence of noise reduction and echo compensation algorithms allowed only if comply with
Loudness, clipping and SNR requirements.
Appendix 2: Call quality metrics table
(recommended)
Metric
Units
Max
Min
Critical
Major
Minor
Warning
Excellent
Mean
Opinion
Score
(audio,
Sevana
AQuA)
-
5
1
<2.5
<3
<3.5
<4
>4
Mean
Opinion
Score
(E-Model)
-
5
1
<3
<3.5
<3.7
<4
<4.3
R Factor
%
100
30
<50
<60
<70
<80
>=80
Speech
distortion
%
0
10
>9
>7
>6.5
>5.5
<4.5
Speech
power
dBm
-17.5
-50
<-50 or
>15
<-40 or
>10
<-35 or
>5
<-30.5 or
>0
<-30
DTMF
detection
ratio
%
100
0
<70
<80
<90
<100
Copyright © Sevana, 2013
Sevana Oy
Agricolankatu 11
00530 Helsinki
Finland
Phone: +358 9 2316 4165
Sevana Oü
Rohtlaane 12
76911 Huuru kula
Estonia (Harjumaa)
Phone: +372 53485178
100