Using Autoregressive Models for Real-Time Packet Loss Concealment in Networked Music Performance Applications

Author:

Sacchetto Matteo1,Huang Yuen1,Bianco Andrea1,Rottondi Cristina1

Affiliation:

1. Dept. of Electronics and Telecommunications, Politecnico di Torino, Italy

Publisher

ACM

Reference12 articles.

1. From artificial neural networks to deep learning for music generation: history, concepts and trends

2. Chin-Jui Chang Chun-Yi Lee and Yi-Hsuan Yang. 2021. Variable-length music score infilling via XLNet and musically specialized positional encoding. arXiv preprint arXiv:2108.05064(2021). Chin-Jui Chang Chun-Yi Lee and Yi-Hsuan Yang. 2021. Variable-length music score infilling via XLNet and musically specialized positional encoding. arXiv preprint arXiv:2108.05064(2021).

3. Peter Foster Anssi Klapuri and Mark D Plumbley. 2011. Causal Prediction of Continuous-Valued Music Features.. In ISMIR. Citeseer 501–506. Peter Foster Anssi Klapuri and Mark D Plumbley. 2011. Causal Prediction of Continuous-Valued Music Features.. In ISMIR. Citeseer 501–506.

4. A case for network musical performance

5. Akira Maezawa . 2019 . Deep linear autoregressive model for interpretable prediction of expressive tempo . Proc. SMC (2019), 364–371. Akira Maezawa. 2019. Deep linear autoregressive model for interpretable prediction of expressive tempo. Proc. SMC (2019), 364–371.

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