Networked Music Performance (NMP) is envisioned as a potential game changer among Internet applications: it aims at revolutionizing the traditional concept of musical interaction by enabling remote musicians to interact and perform together through a telecommunication network. Ensuring realistic conditions for music performance, however, constitutes a significant engineering challenge due to extremely strict requirements in terms of audio quality and, most importantly, network delay. To minimize the end-to-end delay experienced by the musicians, typical implementations of NMP applications use un-compressed, bidirectional audio streams and leverage UDP as transport protocol. Being connection less and unreliable,audio packets transmitted via UDP which become lost in transit are not re-transmitted and thus cause glitches in the receiver audio playout. This article describes a technique for predicting lost packet content in real-time using a deep learning approach. The ability of concealing errors in real time can help mitigate audio impairments caused by packet losses, thus improving the quality of audio playout in real-world scenarios.

中文翻译：

---

网络音乐表演应用中音频信号低延迟数据包丢失隐藏的深度学习方法

网络音乐表演 (NMP) 被设想为互联网应用程序中潜在的游戏规则改变者：它旨在通过远程音乐家通过电信网络进行互动和一起表演，从而彻底改变音乐互动的传统概念。然而，由于对音频质量和最重要的网络延迟的要求极其严格，因此确保音乐表演的真实条件构成了重大的工程挑战。为了最大限度地减少音乐家经历的端到端延迟，NMP 应用程序的典型实现使用未压缩的双向音频流并利用 UDP 作为传输协议。连接少且不可靠，通过 UDP 传输的在传输过程中丢失的音频数据包不会重新传输，从而导致接收器音频播放中出现故障。本文介绍了一种使用深度学习方法实时预测丢失数据包内容的技术。实时隐藏错误的能力可以帮助减轻由数据包丢失引起的音频损伤，从而提高现实场景中的音频播放质量。