论文标题
TPLCNET：实时深层数据包损失隐藏在时间域中使用的短暂时间上下文
tPLCnet: Real-time Deep Packet Loss Concealment in the Time Domain Using a Short Temporal Context
论文作者
论文摘要
本文引入了实时时间域数据包丢失隐藏（PLC）神经网络（TPLCNET）。它有效地预测了以序列对（SEQ2ONE）方式从短上下文缓冲区中的丢失帧。由于其SEQ2ONE结构，因此不需要对模型的连续推断，因为当实际检测到数据包丢失时可以触发。它经过了64小时的开源语音数据和Audio PLC挑战提供的真实呼叫的数据包痕迹的培训。本文描述的最低复杂性的模型达到了强大的PLC性能，并且对所有指标的零基线基线的一致改进。在盲目测试集中的PLC-MOS方面，与零填充基线相比，具有更高复杂性的配置已提交给PLC挑战，并显示出1.07的性能提高，并在挑战排名中获得了竞争性的第三名。
This paper introduces a real-time time-domain packet loss concealment (PLC) neural-network (tPLCnet). It efficiently predicts lost frames from a short context buffer in a sequence-to-one (seq2one) fashion. Because of its seq2one structure, a continuous inference of the model is not required since it can be triggered when packet loss is actually detected. It is trained on 64h of open-source speech data and packet-loss traces of real calls provided by the Audio PLC Challenge. The model with the lowest complexity described in this paper reaches a robust PLC performance and consistent improvements over the zero-filling baseline for all metrics. A configuration with higher complexity is submitted to the PLC Challenge and shows a performance increase of 1.07 compared to the zero-filling baseline in terms of PLC-MOS on the blind test set and reaches a competitive 3rd place in the challenge ranking.
