1. Analysis of on-off patterns in VoIP and their effect on voice traffic aggregation Wenyu Jiang, Henning Schulzrinne Department of Computer Science Columbia University Computer Communications and Networks, 2000. Proceedings. Ninth International Conference on Estimation of Token Bucket Parameters of VoIP Traffic R. Bruno, R.G.Garroppo and S.Giordano Department of Information Engineering University of Pisa High Performance Switching and routing, 2000. ATM 2000 Proceedings of the IEEE Conference on, 2000

2. Outline Introduction Experiment Setup Comparisons with Traditional Silence Detectors Token bucket simulations and results Conclusions

3. Introduction Human speech consists of talk-spurts and silence gaps, also known as on-off patterns. Allows higher bandwidth utilization through multiplexing. Allows per-spurt play out delay adjustment. Enable echo suppression based on silence detector output.

4. Introduction (cont’d) energy Pre-spurt hangover time Post-spurt hangover time time Max (-20db) Min (-45db)

5. Experiment Setup 3COM Ethernet Phone Mediatrix gateway

6. Comparisons with Traditional Silence Detectors Example spurt/gap distributions NeVoT SD spurt and gap CDF using different parameters Spurt/gap distribution after averaging over many conversations

7. Example spurt/gap distributions

10. NeVot SD spurt and gap CDF using different thresholds

13. Spurt/gap distribution after averaging over many conversations

16. Token bucket simulations and results

17. Effect of spurt/gap distribution on multiplexing performance, G.729B

20. Multiplexing performance for NeVoT SD with default parameters

24. Equivalent Queuing Model

25. Birth-and-death Markov Chain

26. Simulation analysis classifier token-bucket conditioner Multiplexer

28. Conclusions Spurt/gap distributions are not exactly exponential, particularly for gaps. The token bucket simulations result indicate that the exponential model generally gives a close estimate of the out-of-profile probability.