Fetching the paper…
Reading the bibliography…
In this work, we propose a permutation invariant language model, SymphonyNet, as a solution for symbolic symphony music generation.
P. Gage, “A new algorithm for data compression,” The C Users Journal , vol. 12, no. 2, pp. 23–38, 1994
1994
Earlier work this paper cites.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2016, pp. 1715–1725
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
G. Hadjeres, F. Pachet, and F. Nielsen, “DeepBach: a steerable model for bach chorales generation,” in International Conference on Machine Learning . PMLR, 2017, pp. 1362–1371
2017
Earlier work this paper cites.
L. Yang, S. Chou, and Y. Yang, “MidiNet: A convolutional generative adversarial network for symbolic-domain music generation,” in Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017, Suzhou, China, October 23-27, 2017 , 2017, pp. 324–331
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
A. Roberts, J. Engel, C. Raffel, C. Hawthorne, and D. Eck, “A hierarchical latent vector model for learning long-term structure in music,” in International conference on machine learning . PMLR, 2018, pp. 4364–4373
2018
Earlier work this paper cites.
C.-Z. A. Huang, A. Vaswani, J. Uszkoreit, I. Simon, C. Hawthorne, N. Shazeer, A. M. Dai, M. D. Hoffman, M. Dinculescu, and D. Eck, “Music transformer: Generating music with long-term structure,” in International Conference on Learning Representations , 2018
2018
Earlier work this paper cites.
H.-W. Dong, W.-Y. Hsiao, L.-C. Yang, and Y.-H. Yang, “Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment,” in Thirty-second aaai conference on artificial intelligence , 2018
2018
Cited alongside, same era.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
C. Payne, “MuseNet,” OpenAI Blog , vol. 3, 2019
2019
Cited alongside, same era.
C. Donahue, H. H. Mao, Y. E. Li, G. W. Cottrell, and J. J. McAuley, “Lakhnes: Improving multi-instrumental music generation with cross-domain pre-training,” in Proceedings of the 20th International Society for Music Information Retrieval Conference, ISMIR 2019, Delft, The Netherlands, November 4-8, 2019 , 2019
2019
Cited alongside, same era.
H. Liang, W. Lei, P. Y. Chan, Z. Yang, M. Sun, and T.-S. Chua, “Pirhdy: Learning pitch-, rhythm-, and dynamics-aware embeddings for symbolic music,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 574–582
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret, “Transformers are rnns: Fast autoregressive transformers with linear attention,” in International Conference on Machine Learning . PMLR, 2020, pp. 5156–5165
2020
Later among the works it cites.
W.-Y. Hsiao, J.-Y. Liu, Y.-C. Yeh, and Y.-H. Yang, “Compound word transformer: Learning to compose full-song music over dynamic directed hypergraphs,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2021, pp. 178–186
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Ren, J. He, X. Tan, T. Qin, Z. Zhao, and T.-Y. Liu, “Popmag: Pop music accompaniment generation,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 1198–1206
2020
Cited alongside, same era.
Y.-S. Huang and Y.-H. Yang, “Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 1180–1188
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Later among the works it cites.
Z. Wang and G. Xia, “MuseBERT: Pre-training music representation for music understanding and controllable generation,” in Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR 2021, Online, November 7-12, 2021 , 2021
2021
Later among the works it cites.
M. Zeng, X. Tan, R. Wang, Z. Ju, T. Qin, and T. Liu, “Musicbert: Symbolic music understanding with large-scale pre-training,” in Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, August 1-6, 2021 , ser. Findings of ACL, vol. ACL/IJCNLP 2021. Association for Computational Linguistics, 2021, pp. 791–800. [Online]. Available: https://doi.org/10.18653/v1/2021.findings-acl.70
2021
Later among the works it cites.
H. Dong, C. Donahue, T. Berg-Kirkpatrick, and J. J. McAuley, “Towards automatic instrumentation by learning to separate parts in symbolic multitrack music,” in Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR 2021, Online, November 7-12, 2021 , 2021
2021
Later among the works it cites.