Does your Voice Quality Monitoring Measure Up?
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1 Does your Voice Quality Monitoring Measure Up? Measure voice quality in real time Today s voice quality monitoring tools can give misleading results. This means that service providers are not getting a true picture of the quality of service their subscribers are experiencing. Persistent quality problems will lead to the users of a service becoming dissatisfied and moving to other service providers. It is essential to pro-actively monitor the quality of every call made in a network in order that quality issues can be detected and resolved immediately. Overprovisioning can reduce the rate of occurrence of quality problems however this can lead to excessively high network operating costs. Service providers, whether phone companies or the network managers of major corporations, need to be able to accurately measure service quality in real time for most or all calls made through their networks. This has been made possible with the introduction of VQmon, an emerging standard for voice quality monitoring. VQmon solves the two major problems with existing voice quality measurement techniques:- Typical network impairments are time varying, which means that voice quality varies during a call. Current generation voice quality measurement tools are not able to predict the impact that time varying quality has on the listener. VQmon is the first monitoring system to model the effects of time varying impairments on subjective voice quality. PSQM, PESQ and PAMS tests are intrusive, which means that test calls must be made through the network in order to make measurements. This uses network bandwidth and actually reduces quality. It is only feasible to make a small number of such test calls and to get occasional snapshots of network performance. VQmon is a non-intrusive technique and can monitor the quality of every call made in the network. How Bursty is your packet loss Packet loss often occurs as a result of congestion or rerouting in the IP network, and hence lost packets are likely to occur in bursts. The degree of burstiness has a major impact on voice quality the packet loss concealment algorithms used by Voice over IP implementations can disguise isolated lost packets but are much less effective when a series of packets are lost. Figure 1 shows some actual recorded packet loss information from the Internet. A well designed and properly managed IP network should provide better performance however many of the same packet loss mechanisms will still apply.
2 Figure 1. Recorded packet loss information from the Internet (Source University of Massachusetts) As a simple example, consider a 3 minute Voice over IP phone call during which there is a two second period of time during which 50% of the packets are lost. Other voice quality monitoring techniques would average this over the call, assuming that a uniform 0.5% of packets are being lost. This would result in the incorrect prediction that the subscriber would not hear any degradation in quality. MOS Packet Loss (%) Random Burst Figure 2 Subjective Quality for Burst vs Random Packet Loss (G.711 PLC) Source AT&T contribution to T1A1.7 May 1999
3 Figure 2 shows the results of subjective tests conducted by AT&T that highlight the effects of packet loss burstiness. A listening panel was asked to rate audio files impaired by both random and burst packet loss for various packet loss rates. At very low packet loss rates burst packet loss can sound better than random loss as the gaps between loss events become large. At packet loss rates in excess of 3% it is clear that burst packet loss has a much more significant effect on voice quality than random packet loss. The perceptual effects of changing quality Figure 3. Comparison of real and perceived voice quality profiles Source France Telecom contribution to ITU SG12 May 2000 If quality changes from good to bad during a call then you would expect that the listener would initially not be too concerned. As the period of poor quality continues then the listener would become more annoyed or distracted. This phenomena was observed in subjective tests conducted by France Telecom in which they asked listeners to continuously rate quality during a call. Figure 3 shows the results of a test in which a call was initially bad (MOS 1.5), changed to good (MOS 4.5) for 30 seconds and then returned to bad. The solid line shows the measured call quality and the dashed line shows the instantaneous call quality rating given by listeners. This example illustrates that it is important to properly model the way that quality is changing and then to model the perceptual effects of these changes in order to understand their effects on the listener or subscriber.
4 Human Memory Effects Recency During subjective testing researchers found that the call quality reported by a listener depended on the time delay between a significant burst of noise and the end of the call. This is believed to be due to the recency effect which relates to a human characteristic of tending to remember only the most recent events and possibly to short term auditory memory decay. Figure 3 shows that this effect can be quite significant. AT&T found that moving a burst of noise from the beginning to the end of a 60 second call changed the reported MOS score from 3.82 to seconds Start of call End of call MOS 3.8 MOS 3.3 MOS 3.2 Noise burst Figure 4 Recency Effect subjective quality is lower if noise occurs near the end of a call Comparing VQmon to PSQM and PAMS One important measure of the effectiveness of voice quality measurement algorithms is their ability to predict the subjective quality score that people would assign. A recent study compared VQmon with the widely used PSQM and PAMS objective measurement algorithms. Six sets of audio files were used in the study, each containing five impaired files. Listeners were asked to rank each set of five files from best to worst and then the average rank was calculated for each file. The three voice quality measurement algorithms were then used to perform the same ranking.
5 2.5 Worst 2 Mean Rank Distance Best VQmon PSQM PAMS Figure 5 Results of subjective test comparing VQmon with PSQM and PAMS The mean rank distance was calculated for each file set and for each measurement algorithm. This givens a measure of how closely the measurement algorithms predicted the ranking given by listeners. As an example, if the order given by the human listeners was and the order given by one of the measurement algorithms was the same the mean rank distance would be 0; if the order given by the measurement algorithm was was then the mean rank distance would be 0.4. The results from the subjective test are given in Figure 5. For five of the six file sets VQmon was more accurate in predicting the rank given by the human listeners. This is particularly impressive when you consider that PSQM and PAMS operate by comparing the original unimpaired audio file with the impaired file whereas VQmon is only operating by analyzing the pattern of packet loss events. Do you need Accurate Voice Quality Metrics? If you need to know what voice quality your customers are experiencing then you need accurate metrics. VQmon is the only voice quality monitoring system that models the effects of time varying impairments. VQmon monitors voice quality in real time and properly models the effects of time varying impairments on perceived quality. If you wait until your customers are letting you know about voice quality problems then it is too late. You need real time monitoring of voice quality to let you manage your service effectively. For more information: Copyright Telchemy Incorporated 2001
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