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Modeling the effects of Burst Packet Loss and Recency on Subjective Voice Quality. Alan Clark Telchemy [email protected]. Embedded Monitoring. Need to monitor QoS to provide feedback on network performance / impact on subjective quality - PowerPoint PPT Presentation
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Telchemy – IPtel 2001
Modeling the effects of Burst Packet Loss and Recency on
Subjective Voice Quality
Alan Clark
Telchemy
Telchemy – IPtel 2001
Embedded Monitoring• Need to monitor QoS to provide feedback
on network performance / impact on subjective quality
• Desirable to provide monitoring in the form of a lightweight software agent
• Focus on time varying impairments – burst packet loss and “recency”
Telchemy – IPtel 2001
Embedded Monitoring
IPNetwork
GatewayGateway
VQmon Agent embedded into VoIP Gateway
QoSmetrics
SMS/NMS
Telchemy – IPtel 2001
The E Model• “Mouth to ear” transmission quality
measurement
• Produces an “R” factor typically in the range 50 (bad) -95 (good)
• R factor can be related to MOS score, Terminate Early (TME) etc.
• ITU G.107/ G.108 and ETSI ETR250
Telchemy – IPtel 2001
R Factor vs MOS
50
60
70
80
90
100
0.1 1 10
Percentage of users that terminate calls early
4.5
4.0
3.0
R Factor MOS
Telchemy – IPtel 2001
E Model
R = Ro - Is - Id - Ie + A
Base R value- Noise level
Impairments thatoccur simultaneouslywith speech- received speech level- sidetone level- quantization noise
Impairments thatare delayed withrespect to speech- talker echo- listener echo- round trip delay
Equipment ImpairmentFactor- CODEC- multiplexing effects
Advantage factor
Telchemy – IPtel 2001
Extended E Model
Delay, measuredusing RTCP
NetworkR FactorIe
PacketLoss
Jitter
Codectype
LossModel
JitterModel
CodecModel
Burstmodel
Recencymodel
UserR Factor
Delaymodel
Telchemy – IPtel 2001
Burst vs average loss
Burst of packet lossZero packet loss
Non-bursty packet loss
Telchemy – IPtel 2001
“Recency” effect
“Good” 4.3MOS“Bad” 1.8 MOS(3dB SNR)
MOS 3.82
MOS 3.28
MOS 3.18
Source AT&TT1A1.7/98-031
60 second call
Telchemy – IPtel 2001
Jitter and Packet Loss
ArrivingRTPpackets
Jitterbuffer
Late packetsdiscarded
Monitor jitter andpacket loss afterjitter buffer
CODEC
Telchemy – IPtel 2001
Loss Model - Markov model
3Lost
1Rcvd
P13
P22
P11
P312
Rcvd
P23P32
4Lost
P14
P41
Burst state
Gap state
Model parametersreconstructed at endof call
Telchemy – IPtel 2001
Loss Model - mapping loss to Ie
0
10
20
30
40
50
0 5 10 15
Packet Loss Rate
Ie (p
acke
t los
s)Curve is CODECdependant
Telchemy – IPtel 2001
Determining QoS metrics
t = 5
t = 15
1. Determine “good”and “bad” state Ie Factor
2. EstimateInstantaneousR Factor foreach state
Telchemy – IPtel 2001
Determining QoS metrics
t = 5
t = 15
1. Determine “good”and “bad” state Ie Factor
2. EstimateInstantaneousR Factor foreach state
3. Determine average Ie
Telchemy – IPtel 2001
Delay effects
Source NTT
Conversation
Verify sequenceof random numbers
4
3
2
0 1 2 3 4
MeanOpinionScore
Delay (seconds)
Telchemy – IPtel 2001
Measuring Delay
CODECCODEC
Accumulateframe
Encode
Transmission Jitterbuffer
Decode
RTCP exchange
Telchemy – IPtel 2001
Delay Model
0
10
20
30
0 100 200 300 400
175 mS “knee”
End to end delay (mS)
R FactorReduction
Telchemy – IPtel 2001
Estimation of recency effect
Delay since last significant burst
Effects decay over 30-60 secondsAverage for call
Telchemy – IPtel 2001
Execution model
Call 1 Call 2
InitializePorts
Startcall
Endcall
Startcall
EndcallLoss events Loss events
Typically 1.4 assemblylanguage instructionsper packet
Telchemy – IPtel 2001
Integration with VoIP SMS
VQmonSNMPSet, Get, Trap
End of callmsg (DRQ)
CDRServiceMgtSystem
NetworkMgtSystem
Telchemy – IPtel 2001
Ranking accuracy – Set 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 2 3 4 5 6 7
Predictor (1=VQmon, 2-7=listeners)
Acc
ura
cy
Telchemy – IPtel 2001
Ranking Accuracy – Set 2
00.10.20.30.40.50.60.70.80.91
1 2 3 4 5 6 7
Predictor (1=VQmon, 2-7=listeners)
Acc
ura
cy
Telchemy – IPtel 2001
Ranking Accuracy – Set 4
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 2 3 4 5 6 7
Predictor (1=VQmon, 2-7=listeners)
Acc
ura
cy
Telchemy – IPtel 2001
Conclusions• Computational model meets design goals
• Ranking accuracy is comparable to human listener
• But need - – Systematic comparison with PSQM/ PESQ– Increased level of subjective testing– Add support for VAD, non-PLC– Improved accuracy requires some information
on voice frame content
Telchemy – IPtel 2001
Further work areas• Use CODEC generated frame loss event –
indicate presence of speech energy
• Additional subjective testing– Wider variety of audio sources– Force listeners to focus on call content– Design impairments to isolate recency effect,
burst characteristics, masking effects