Wireless Access Research
Adil Raja April 2006
Contents
• Packet Loss Modeling Approaches. Packet Based Approaches.
Speech Based Approaches.
• A Problem (Speech Based).
• A Solution (Packet Based).
Wireless Access Research
Adil Raja April 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}
Wireless Access Research
Adil Raja April 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.
Wireless Access Research
Adil Raja April 2006
Non-intrusive PESQTest-Packets
Pseudo-Test-Packets
Reference
Wireless Access Research
Adil Raja April 2006
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).
Wireless Access Research
Adil Raja April 2006
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.
Wireless Access Research
Adil Raja April 2006
Issues related to CELP Codec’s
• Loss of representation Due to. Quantization of LSPs. Narrow bandwidth of G.729 codec. (200-
3400KHz).
Wireless Access Research
Adil Raja April 2006
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 …
Wireless Access Research
Adil Raja April 2006
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.
Wireless Access Research
Adil Raja April 2006
The Gilbert Elliot Model
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1-q
No Loss State
1-p X=1 X=0q
Loss State
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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
Wireless Access Research
Adil Raja April 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.
Wireless Access Research
Adil Raja April 2006
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.
Wireless Access Research
Adil Raja April 2006
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.
Wireless Access Research
Adil Raja April 2006
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.
Wireless Access Research
Adil Raja April 2006
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
Wireless Access Research
Adil Raja April 2006
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.