New Models for Perceived Voice Quality Prediction and their Applications in Playout Buffer Optimization for VoIP Networks

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1 New Models for Perceived Voice Quality Prediction and their Applications in Playout Buffer Optimization for VoIP Networks Dr. Lingfen Sun Prof Emmanuel Ifeachor University of Plymouth United Kingdom {L.Sun;

2 Outline Background Speech quality for VoIP networks Current status Aims of the project Main Contributions Novel non-intrusive voice quality prediction models Novel perceptual-based speech quality optimization (e.g. jitter buffer optimization) mechanism Conclusions and Future Work ICC 2004, Paris France, June

3 Background Speech Quality for VoIP Networks Reference speech SCN Gateway MOS Intrusive measurement IP Network Degraded speech Gateway SCN SCN: Switched Comm. Networks (PSTN, ISDN, GSM ) Non-intrusive measurement MOS End-to-end Perceived speech quality VoIP speech quality: end-user perceived quality (MOS), an important metric. Affected by IP network impairments and other impairments. Voice quality measurement: subjective (MOS ) or objective (intrusive or non-intrusive) ICC 2004, Paris France, June

4 Current Status and Problems Lack of an efficient non-intrusive speech quality measurement method E-model (a complicated computational model) Based on subjective tests to derive models/parameters, timeconsuming and expensive. Only limited models exist Lack of perceptual optimization control methods only based on individual network parameters for buffer optimization and QoS control purposes not perceptual-based optimization control ICC 2004, Paris France, June

5 Aims of the Project End-to-end perceived voice quality (MOS) Encoder Packetizer IP Network Depacketizer Jitter buffer Decoder Voice source Sender Non-intrusive measurement Receiver Voice receiver MOS To develop novel and efficient method/models for non-intrusive quality prediction, To apply the models for perceptual-based optimization control ( e.g. buffer optimization or adaptive sender-bit-rate QoS control). ICC 2004, Paris France, June

6 Novel Non-intrusive Voice Quality Prediction Intrusive method MOS(PESQ) delay E-model Measured MOSc Reference speech PESQ Degraded speech VoIP Network (packet loss, delay, codec ) Non-intrusive method New model (regression or ANN models) Predicted MOSc Based on intrusive quality measurement (e.g. PESQ) to predict voice quality non-intrusively which avoids subjective tests. A generic method which can be applied to audio, image and video. ICC 2004, Paris France, June

7 New Structure to Obtain MOS c Reference speech Degraded speech PESQ MOS (PESQ) MOS R I e I e E-model MOSc End-to-end delay Delay model I d PESQ can only predict one-way listening speech quality (expressed as MOS). By a new combined PESQ/E-model structure, a conversational speech quality (MOSc) can be obtained as Measured MOSc. ICC 2004, Paris France, June

8 Regression based Models (1) Codec Packet loss Delay (d) I e model I d model I e I d E-model MOSc (a) PESQ/ PESQ-LQ MOS (PESQ) MOS R I e Measured I e Speech database Encoder Loss model Decoder Degraded speech Reference speech (b) Nonlinear regression model (I e model) Predicted I e Nonlinear regression models are derived for I e based on PESQ/PESQ-LQ Further combine I e with I d to obtain MOS c. ICC 2004, Paris France, June

9 Regression based Models (2) I e can be modelled by a logarithm fitting function with the form of I e = aln( 1+ bρ) + c Parameters for different codecs (PESQ) Parameters AMR(H) AMR(L) G.729 G ilbc a b* c ICC 2004, Paris France, June

10 Regression Models for AMR (12.2Kb/s) e.g. for AMR (12.2Kb/s), I e = 16.68ln( ρ ) The goodness of fit is: SSE = 2.83 and R 2 = MOS vs. packet loss and delay ICC 2004, Paris France, June

11 Perceptual-based Buffer Optimization Motivation: only based on individual network parameters (e.g. delay or loss) targeting only minimum average delay or minimum late arrival loss, not maximum MOS. There is a need to design buffer algorithm to achieve optimum perceived speech quality. Contribution A perceptual-based optimization jitter buffer algorithm o Use regression based models for buffer optimization o Use a minimum impairment criterion instead of traditional maximum MOS score o A Weibull delay distribution based on trace analysis o A perceptual-based optimization of playout buffer algorithm ICC 2004, Paris France, June

12 Impairment Function I m Define: impairment function I m I = f ( d, ρ) = I + I m d eρ = 0.024d ( d 177.3) H ( d 177.3) + aln(1 + bρ) H ( x) = 0 if x < 0 where a and b are codec related parameters H ( x) = 1 if x 0 (( d µ ) / α ) ρ = ρ + ρ = ρ + (100 ρ ) P( X d) = ρ + (100 ρ ) e n b n n n n r buffer loss ρ b Weilbull distribution Playout delay d ICC 2004, Paris France, June

13 Minimum Impairment Criterion Define: minimum impairment criterion Given: network delay d n, network loss ρ n and codec type Estimate: an optimized playout delay d opt Such that: minimize I m can be reached. d 1 d 2 d 3 d 4 Minimum I m ICC 2004, Paris France, June

14 Perceptual-based Optimization Buffer Algorithm For every packet i received, calculate network delay n i If mode == SPIKE then if n i tail*old_d then mode = NORMAL elseif n i > head*d i then mode = SPIKE; old_d = d i else -update delay records for the past W packets endif At the beginning of a talkspurt If mode == SPIKE then d i = n i else -obtain (µ, α, γ) for Weilbull distribution for the past W packets -search playout d which meets minimum I m criterion endif ICC 2004, Paris France, June

15 Performance Analysis and Comparison (1) Trace Delay (ms) Jitter (ms) Loss (%) Selected five traces from UoP to CU (USA), DUT (Germany), BUPT (China), and NC (China). Traces 1 and 3 with high delay variation and traces 2, 4, 5 with low delay variation ICC 2004, Paris France, June

16 Performance Analysis and Comparison (2) Performance comparison for buffer algorithms 4 MOS Traces exp-avg fast-exp min-delay spk-delay adaptive p-optimum p-optimum algorithm achieves the optimum voice quality for all traces. adaptive algorithm achieves sub-optimum quality with low complexity. ICC 2004, Paris France, June

17 Conclusions and Future Work Conclusions The development of a new methodology and regression models to predict voice quality non-intrusively. Demonstrated the application of new non-intrusive voice quality prediction models to perceptual-based optimization of playout buffer algorithms. Future Work To consider buffer adaptation during a talkspurt in order to achieve the best trade-off between delay, loss and end-to-end jitter. To extend the work to improve the performance of multimedia services (e.g. audio/image/video) over IP networks ICC 2004, Paris France, June

18 Contact Details Dr. Lingfen Sun Prof Emmanuel Ifeachor Any questions? Thank you! ICC 2004, Paris France, June

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