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Symbolic Music Generation relies on the contextual representation capabilities of the generative model, where the most prevalent approach is the Transformer-based model.
2016
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2017
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2017
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A. Roberts, J. H. Engel, C. Raffel, C. Hawthorne, and D. Eck, “A hierarchical latent vector model for learning long-term structure in music,” in Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 , ser. Proceedings of Machine Learning Research, J. G. Dy and A. Krause, Eds., vol. 80. PMLR, 2018, pp. 4361–4370. [Online]. Available: http://proceedings.mlr.press/v80/roberts18a.html
2018
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H. Dong, W. Hsiao, L. Yang, and Y. Yang, “Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18) . AAAI Press, 2018, pp. 34–41. [Online]. Available: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17286
2018
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C. 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 7th International Conference on Learning Representations, ICLR 2019 . OpenReview.net, 2019. [Online]. Available: https://openreview.net/forum?id=rJe4ShAcF7
2019
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C. M. Payne, “Musenet,” https://openai.com/blog/musenet
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2019
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J. Briot, G. Hadjeres, and F. Pachet, Deep Learning Techniques for Music Generation . Springer, 2020. [Online]. Available: https://doi.org/10.1007/978-3-319-70163-9
2020
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2019
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Q. Wang, F. Su, and Y. Wang, “A hierarchical attentive deep neural network model for semantic music annotation integrating multiple music representations,” in Proceedings of the 2019 on International Conference on Multimedia Retrieval, ICMR 2019 . ACM, 2019, pp. 150–158. [Online]. Available: https://doi.org/10.1145/3323873.3325031
2019
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M. C. McCallum, “Unsupervised learning of deep features for music segmentation,” in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2019 . IEEE, 2019, pp. 346–350. [Online]. Available: https://doi.org/10.1109/ICASSP.2019.8683407
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C. Hawthorne, A. Stasyuk, A. Roberts, I. Simon, C. A. Huang, S. Dieleman, E. Elsen, J. H. Engel, and D. Eck, “Enabling factorized piano music modeling and generation with the MAESTRO dataset,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019. [Online]. Available: https://openreview.net/forum?id=r1lYRjC9F7
2019
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S. Wu and Y. Yang, “The jazz transformer on the front line: Exploring the shortcomings of ai-composed music through quantitative measures,” in Proceedings of the 21th International Society for Music Information Retrieval Conference, ISMIR 2020, Montreal, Canada, October 11-16, 2020 , J. Cumming, J. H. Lee, B. McFee, M. Schedl, J. Devaney, C. McKay, E. Zangerle, and T. de Reuse, Eds., 2020, pp. 142–149. [Online]. Available: http://archives.ismir.net/ismir2020/paper/000339.pdf
2020
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2021
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A. Muhamed, L. Li, X. Shi, S. Yaddanapudi, W. Chi, D. Jackson, R. Suresh, Z. C. Lipton, and A. J. Smola, “Symbolic music generation with transformer-gans,” in Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021 . AAAI Press, 2021, pp. 408–417. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/16117
2021
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S. Dai, Z. Jin, C. Gomes, and R. B. Dannenberg, “Controllable deep melody generation via hierarchical music structure representation,” in Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR 2021, Online, November 7-12, 2021 , J. H. Lee, A. Lerch, Z. Duan, J. Nam, P. Rao, P. van Kranenburg, and A. Srinivasamurthy, Eds., 2021, pp. 143–150. [Online]. Available: https://archives.ismir.net/ismir2021/paper/000017.pdf
2021
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2021
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