Modelling the Effect of Packet Loss on Speech Quality

Wireless Access Research

Adil Raja April 2006

Modeling the Effect of Packet Loss on Speech Quality



Contents

• Packet Loss Modeling Approaches. Packet Based Approaches.

Speech Based Approaches.

• A Problem (Speech Based).

• A Solution (Packet Based).



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}



Speech Based Approaches

• Intrusive - ITU-T Recommendation P.862 (PESQ).

• Non-intrusive – ITU-T Recommendation P.563 (PSEAM).

• Non-intrusive PESQ – A. E. Conway.



Non-intrusive PESQTest-Packets

Pseudo-Test-Packets

Reference



Drawbacks

• The scheme is only suitable for estimating the effect of packet-loss/frame erasure.

• Does not truly capture the effect of packet loss. (Importance of packet).



A Speech Based Approach

• Insertion of a loss-representative packet/frame in loss locations.

• The loss representative packet should have a high auditory distance from the reference code-book.

• A loss representative signal was created by warping the poles of a voiced speech.



Auditory Distance of Loss representative Signal



Results for 16-bit Linear PCM

r=0.9117



Issues related to CELP Codec’s

• Loss of representation Due to. Quantization of LSPs. Narrow bandwidth of G.729 codec. (200-

3400KHz).



P.563 vs Packet Loss

• Measures effect of packet loss for CELP codecs.

• Packet loss detection is based on time domain cross-correlation of successive speech frames.

• r=0.635 with PESQ.

• Conformance of P.563 …



The Solution – Packet Based

• Useful packet loss Metrics. Mean Loss Rate Mean Burst Length Conditional Loss Probability Inter Loss Distance/Gap Length.

• Packet loss is normally modeled using a Gilbert Model.



The Gilbert Elliot Model

0

1

1

mmpn

i

i

p

1-q

No Loss State

1-p X=1 X=0q

Loss State

1

1

1

2

)1(1n

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



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.



Modeling the Effect of Packet Loss on Speech Quality

• Modeling the Effect of Packet Loss on Speech Quality is a regression problem.

• Correct choice of input parameters is vital for good approximations.

• A good regression model can fails if the input variables are not correctly selected.

• The converse is true.



Existing Approaches

• L. F. Sun’s variables and results. Mean Loss Rate, Codec, gender, ulp(VAD), clp(VAD). Correlation coefficient: - 0.967 (training), 0.952 (testing). (vs

PESQ). Average error 0.19.

• S. Mohammed’s variables and results Mean loss rate, burst size, codec (limited codecs), Packetization

Intervals. Correlation coefficient: - 0.73 -0.93 (vs subjective tests). Random Neural Network.



My Approach

• Codecs – G.729 and G.723.1.• 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}.• Regression is performed using a (8-6-4-1) back propagation Neural Network.• Learning Function: Gradient Descent with momentum.• Hyperbolic Tangent (Activation).• A total of 480 (240x2) speech files out of which 40% were used for training and 60%

were used for validation.• Speech activity – 70-80%.• Correlation coefficient: 0.9874 (training) 0.9807 (validation). • MSE: - 0.019966.



Parameters and ResultsL.F. Sun S. Mohammed The Proposed Method

MLR ♣ ♣ ♣MBL/CLP ♣ ♣ ♣Codec ♣ ♣ ♣FEC ♣PI ♣ ♣/(or Not)

Gender ♣VBL,MGL,VGL ♠Results 0.952 0.93/(0.99 too) 0.9807



Scatter Plots



Error Plot



Conclusions

• To calculate various distribution parameters and keeping a nicely trained NN model.

• A promising solution for mapping the effect of frame erasures on wireless telephony quality and packet loss in VoIP.

• Mapping to Subjective tests – Calculate MOS and retrain the net for the subjective target.

• Useful for Control purposes.• Improvements – Classifying burst/gap lengths.• A speech based solution is an open problem.



Thank you !



S. Mohammed’s Method

L.F. Sun’s Method

The Proposed Method

MLR Yes Yes Yes

MBL/CLP Yes Yes Yes

Codec Yes Yes Yes

FEC Yes

PI Yes Yes

Gender Yes

VBL,MGL,VGL Yes

Modelling the Effect of Packet Loss on Speech Quality

Engineering

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