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

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; E.Ifeachor}@plymouth.ac.uk

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, 20-24 June 2004 2

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, 20-24 June 2004 3

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, 20-24 June 2004 4

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, 20-24 June 2004 5

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, 20-24 June 2004 6

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, 20-24 June 2004 7

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, 20-24 June 2004 8

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.723.1 ilbc a 16.68 30.86 21.14 20.06 12.59 b*100 30.11 4.26 12.73 10.24 9.45 c 14.96 31.66 22.45 25.63 20.42 ICC 2004, Paris France, 20-24 June 2004 9

Regression Models for AMR (12.2Kb/s) e.g. for AMR (12.2Kb/s), I e = 16.68ln(1 + 0.3011ρ ) + 14.96 The goodness of fit is: SSE = 2.83 and R 2 = 0.998 MOS vs. packet loss and delay ICC 2004, Paris France, 20-24 June 2004 10

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, 20-24 June 2004 11

Impairment Function I m Define: impairment function I m I = f ( d, ρ) = I + I m d eρ = 0.024d + 0.11( 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, 20-24 June 2004 12

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, 20-24 June 2004 13

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, 20-24 June 2004 14

Performance Analysis and Comparison (1) Trace Delay (ms) Jitter (ms) Loss (%) 1 153 16.2 1.1 2 46 0.8 0.3 3 186 19.5 14.3 4 16 0.7 4.4 5 150 0.2 0.2 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, 20-24 June 2004 15

Performance Analysis and Comparison (2) Performance comparison for buffer algorithms 4 MOS 3.5 3 2.5 2 1.5 1 0.5 1 2 3 4 5 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, 20-24 June 2004 16

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, 20-24 June 2004 17

Contact Details http://www.tech.plymouth.ac.uk/spmc Dr. Lingfen Sun L.Sun@plymouth.ac.uk Prof Emmanuel Ifeachor E.Ifeachor@plymouth.ac.uk Any questions? Thank you! ICC 2004, Paris France, 20-24 June 2004 18