Jitter Buffer Compensation in Voice over IP Quality Estimation

Tong Mo, Andrew Hines
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引用次数: 4

Abstract

Voice over Internet Protocol (VoIP) communications has grown in popularity and has been widely adopted as an alternative to traditional telephone technologies. As packet delivery time can vary, congestion and poor connections can result in delays being introduced. In order to maximise the Quality of Experience (QoE) for VoIP users, jitter buffers are deployed to manage the speech output at the receiver by minimising stalls in signal playout. Automated computer models have been created to compare the signal at the origin and destination in order to predict the perceived quality. Although short playout adjustments introduced by jitter buffers are imperceptible to human listeners, they can skew the results from speech quality prediction models. In this paper, the influence of origin and destination signal time alignment is investigated. A new algorithm for delay estimation that can be used for Jitter Buffer Compensation is proposed and evaluated for use within a speech quality prediction model called VISQOL. Two experiments were conducted. The first was used to design a delay estimation algorithm and to tune its parameters. The second validates the algorithm performance by comparing the quality prediction accuracy of the VISQOL speech quality model with the proposed Jitter Buffer Compensation model to the baseline model results. The results show that the proposed algorithm produces significantly fewer signal mis-alignments and better quality prediction.
IP语音质量估计中的抖动缓冲补偿
互联网协议语音(VoIP)通信越来越受欢迎,并被广泛采用作为传统电话技术的替代方案。由于数据包的传递时间可以变化,拥塞和不良连接可能导致引入延迟。为了最大限度地提高VoIP用户的体验质量(QoE),通过最小化信号播放中的停顿,部署了抖动缓冲器来管理接收器的语音输出。已经建立了自动计算机模型来比较起点和终点的信号,以预测感知质量。虽然由抖动缓冲器引入的短播放调整对人类听众来说是难以察觉的,但它们可能会扭曲语音质量预测模型的结果。本文研究了源、目的信号时间对准对系统性能的影响。提出了一种用于抖动缓冲补偿的延迟估计新算法,并对其在语音质量预测模型VISQOL中的应用进行了评估。进行了两个实验。首先设计了一种延迟估计算法并对其参数进行了调整。第二步通过将提出的Jitter Buffer Compensation模型与VISQOL语音质量模型的质量预测精度与基线模型结果进行比较,验证算法的性能。结果表明,该算法显著减少了信号错列,提高了预测质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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