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Novel approaches for online playout delay prediction in VoIP applications using time series models

机译:使用时间序列模型在VoIP应用中进行在线播放延迟预测的新颖方法

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摘要

Voice over IP (VoIP) applications requires a buffer at the receiver to minimize the packet loss due to late arrival. Several algorithms are available in the literature to estimate the playout buffer delay. Classic estimation algorithms are non-adaptive, i.e. they differ from more recent approaches basically due to the absence of learning mechanisms. This paper introduces two new formulations of adaptive algorithms for online learning and prediction of the playout buffer delay, the first one being based on the standard Box-Jenkins autoregressive model, while the second one being based on the feedforward and recurrent neural networks. The obtained results indicate that the proposed algorithms present better overall performance than the classic ones.
机译:IP语音(VoIP)应用程序需要在接收器处有一个缓冲区,以最大程度地减少由于延迟到达而造成的数据包丢失。文献中提供了几种算法来估计播出缓冲区延迟。经典的估计算法是非自适应的,即,由于缺少学习机制,它们与最新的方法有所不同。本文介绍了两种新的自适应算法公式,用于在线学习和预测播出缓冲区延迟,第一种基于标准Box-Jenkins自回归模型,第二种基于前馈和递归神经网络。获得的结果表明,所提出的算法具有比传统算法更好的整体性能。

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