A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications
This addresses audio quality issues for remote musicians in networked performances, but it is incremental as it applies existing deep learning techniques to a specific domain problem.
The paper tackles the problem of audio glitches in Networked Music Performance due to UDP packet loss by proposing a deep learning method for real-time packet loss concealment, aiming to improve audio quality in real-world scenarios.
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.