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EnglishNetworked 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 uncompressed, bidirectional audio streams and leverage UDP as transport protocol. Being connectionless and unreliable, audio packets transmitted via UDP which become lost in transit are not retransmitted 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 realworld scenarios.
A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications / Verma, P.; Mezzay, A. I.; Chafe, C.; Rottondi, C.. - ELETTRONICO. - 2020-(2020), pp. 268-275. ((Intervento presentato al convegno 27th Conference of Open Innovations Association FRUCT, FRUCT 2020 tenutosi a Trento, Italia nel 2020.
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| Titolo: | A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | Networked Music Performance (NMP) is envisioned as a potential game changer among Internet applic...ations: 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 uncompressed, bidirectional audio streams and leverage UDP as transport protocol. Being connectionless and unreliable, audio packets transmitted via UDP which become lost in transit are not retransmitted 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 realworld scenarios. | |
| ISBN: | 978-952-69244-3-4 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Real_Time_Network_Audio_Performace (2).pdf | articolo principale | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| Rottondi-Adeep.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2850651
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