This document describes research using genetic programming to model the effect of packet loss on speech quality. It examines previous approaches using packet-based models, speech quality models, and neural networks. The current research uses genetic programming and a Gilbert model of packet loss to simulate loss and develop a model relating loss metrics to mean opinion scores. The model is trained and validated on speech files with varying packet loss, and genetic programming is used to develop functions mapping loss metrics to quality scores.

1. Wireless Access Research
Adil Raja June 2006
Modeling the Effect of Packet
Loss on Speech Quality: GP
Based Symbolic Regression

2. Wireless Access Research
Adil Raja June 2006
Packet Loss Modeling Approaches
• Packet Based Approaches.
 Based on regression of packet loss parameters to MOS.
 Parameters include mean Loss rate, conditional loss probability
etc.
• Some approaches include:
 Markov Models {A. D. Clark}
 Regression Using Artificial Neural Networks.
{L. F. Sun et. al. and S. Mohammed et. Al}

3. Wireless Access Research
Adil Raja June 2006
Speech Based Approaches
• Intrusive - ITU-T Recommendation P.862 (PESQ).
• Non-intrusive – ITU-T Recommendation P.563 (PSEAM).
• Non-intrusive PESQ – A. E. Conway.

4. Wireless Access Research
Adil Raja June 2006
Previous Work
• ANN based Regression of network traffic metrics
on speech quality.
• Useful Network loss Metrics.
 Mean Loss Rate.
 Means and Variances of Burst and Gap Length
Distributions.
 Codec Type and Packetization Interval.
 Inter Loss Distance/Gap Length.
• Packet loss was modeled using a Gilbert Model.
• Results: - rtraining=0.9835; rvalidation=0.9821;
rtesting=0.9763

5. Wireless Access Research
Adil Raja June 2006
The Gilbert Elliot Model
0
1
1
mmp
n
i
i 





 


p
1-q
No Loss
State
1-p X=1 X=0
q
Loss State












 




1
1
1
2
)1(1
n
i
i
n
i
i imimq
Parameters of Geometrically distributed burst/gap lengths
Mean Burst length = 1/q
Variance of Burst Length Distribution = (1-q)/q2
Mean Gap Length = 1/p
Variance of Gap Length = (1-p)/p2
qp
p

1

6. Wireless Access Research
Adil Raja June 2006
The Gilbert Model
• Packet loss can be simulated for certain
values of p and q.
• During network operation bursts have to
be captured for determining clp and ulp.
• The Gilbert model also models the packet
loss due to jitter buffer discard/overflow.

7. Wireless Access Research
Adil Raja June 2006
Current Approach
• Codecs – G.729 and G.723.1 and AMR-NB
• Packet/frame loss simulation – Gilbert Model
• Mean Loss rate ulp and clp were varied between 0-0.85 and 0-0.90
respectively.
• Input Variables
 mean loss rate, mean and variance of burst length distribution (VAD), mean and
variance of gap length distribution (VAD), codec type and packetization interval.
 VAD – Different packets have different importance {L. F. Sun | C. Hoene}.
• Genetic Programming is used for Symbolic Regression.
• Hyperbolic Tangent (Activation).
• A total of 659 speech files out of which 35% were used for training, 15%
were used for validation and 50% were used for Speaker independent
testing.
• Speech activity – 70-80%.

8. Wireless Access Research
Adil Raja June 2006
Genetic Programming (GP)
• GP is a Machine Learning Technique
inspired by biological evolution.

9. Wireless Access Research
Adil Raja June 2006
Fitness: 0.0327 Test Fitness: 0.0437, r= 0.9748, r=0.9635 (tree5)

10. Wireless Access Research
Adil Raja June 2006
Training (t5)

11. Wireless Access Research
Adil Raja June 2006
Validation (t5)

12. Wireless Access Research
Adil Raja June 2006
Fitness: 0.0342, Test Fitness: 0.0424, r=0.9737, r=0.9642

13. Wireless Access Research
Adil Raja June 2006
Fitness=0.0463; Test Fitness=0.0528 r=0.9642, r=0.9566
F(X)=mysqrt(times(8,sin(X3)));
GP-MOS-LQO= -1.2442 * F(X) + 3.7511

14. Wireless Access Research
Adil Raja June 2006
Training tn(28)

15. Wireless Access Research
Adil Raja June 2006
Validation tn(28)

16. Wireless Access Research
Adil Raja June 2006
Fitness=0.0466; Validation Fitness=0.0527; Testing Fitness=0.0401 r=0.9640, 0.9567,
r=0.9696.
F(X)=minus(X3,times(5,kozasqrt(X3)));
GP-MOS-LQO= 0.8325*F(X) + 3.8482

17. Wireless Access Research
Adil Raja June 2006
Training tn(25)

18. Wireless Access Research
Adil Raja June 2006
Validation tn(25)

19. Wireless Access Research
Adil Raja June 2006
Testing tn(25)