论文标题
部分可观测时空混沌系统的无模型预测
Improving performance of real-time full-band blind packet-loss concealment with predictive network
论文作者
论文摘要
数据包丢失隐藏（PLC）是一种工具，用于增强由于网络条件差或音频处理管道中的底流量/溢出而引起的语音退化。我们提出了一种实时复发方法，该方法利用先前的输出来减轻丢失的数据包的人工制作，而无需先前了解丢失面具。拟议的全带复发网络（FRN）模型在48 kHz的运行量，适用于高质量的电信应用程序。实验结果突出了FRN在最近的PLC挑战中的优势比离线非可乐基线的优越性和表现最佳。
Packet loss concealment (PLC) is a tool for enhancing speech degradation caused by poor network conditions or underflow/overflow in audio processing pipelines. We propose a real-time recurrent method that leverages previous outputs to mitigate artefact of lost packets without the prior knowledge of loss mask. The proposed full-band recurrent network (FRN) model operates at 48 kHz, which is suitable for high-quality telecommunication applications. Experiment results highlight the superiority of FRN over an offline non-causal baseline and a top performer in a recent PLC challenge.
