# A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications

arXiv: https://arxiv.org/abs/2007.07132 · HTML: https://mlsanity.com/paper/2007.07132/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [A Generative Model for Raw Audio Using Transformer Architectures](https://mlsanity.com/paper/2106.16036/) (2021)
- [Whisper-GPT -- Continuous Discrete Hybrid Representation Language Models For Speech And Music](https://mlsanity.com/paper/2412.11449/) (2024)

## Abstract (arXiv)

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.