  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.
