[1] L.-C. Yang, S.-Y. Chou, and Y.-H. Yang, “Midinet: A convolutional generative adversarial network for symbolic-domain music generation,” in Proceeding of the 18th International Society for Music Information Retrieval Conference, 2017.
[2] 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 Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, 2018.
[3] C. Donahue, H. H. Mao, Y. E. Li, G. W. Cottrell, and J. McAuley, “Lakhnes: Improving multiinstrumental music generation with cross-domain pre-training,” in Proceeding of the 20th International Society for Music Information Retrieval Conference, 2019.
[4] 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, pp. 1198–1206, 2020.
[5] H.-W. Dong, K. Chen, S. Dubnov, J. McAuley, and T. Berg-Kirkpatrick, “Multitrack music transformer,” in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1–5, 2023.
[6] A. Vivaldi, “Concerto in B minor.” L’estro armonico, Op.3, no.10, RV 580, 1711.
[7] J. Ens and P. Pasquier, “Mmm : Exploring conditional multi-track music generation with the transformer,” 2020.
[8] J. Liu, Y. Dong, Z. Cheng, X. Zhang, X. Li, F. Yu, and M. Sun, “Symphony generation with permutation invariant language model,” in Proceeding of the 23rd International Society for Music Information Retrieval Conference, 2022.
[9] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. ukasz Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems, vol. 30, Curran Associates, Inc., 2017. 74
[10] A. Laaksonen, K. Lemström, and O. Björklund, “Transposition and time-scaling invariant algorithm for detecting repeated patterns in polyphonic music,” in Mathematics and Computation in Music, p. 168–179, 2022.
[11] E. Karystinaios and G. Widmer, “Cadence detection in symbolic classical music using graph neural networks,” in Proceeding of the 23rd International Society for Music Information Retrieval Conference, 2022.
[12] D. Jeong, T. Kwon, Y. Kim, and J. Nam, “Graph neural network for music score data and modeling expressive piano performance,” in International Conference on Machine Learning, 2019.
[13] W. M. Szeto and M. H. Wong, “A graph-theoretical approach for pattern matching in post-tonal music analysis,” Journal of New Music Research, vol. 35, 12 2006.
[14] 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, vol. 35, pp. 178–186, 2021.
[15] M. Zeng, X. Tan, R. Wang, Z. Ju, T. Qin, and T.-Y. Liu, “Musicbert: Symbolic music understanding with large-scale pre-training,” in Findings of the Association for Computational Linguistics: ACLIJCNLP 2021, pp. 791–800, 2021.
[16] S. Oore, I. Simon, S. Dieleman, D. Eck, and K. Simonyan, “This time with feeling: Learning expressive musical performance,” Neural Computing and Applications, pp. 1–13, 2018.
[17] I. Simon and S. Oore, “Performance rnn: Generating music with expressive timing and dynamics.” https://magenta.tensorflow.org/performance-rnn, 2017.
[18] 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 7th International Conference on Learning Representations, 2019.
[19] C. Payne, “Musenet..” http://openai.com/blog/musenet, Apr. 2019.
[20] 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, pp. 1180–1188, 2020. 75
[21] S.-L. Wu and Y.-H. Yang, “Musemorphose: Full-song and fine-grained piano music style transfer with one Transformer VAE,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 1953–1967, 2023.
[22] Y.-J. Shih, S.-L. Wu, F. Zalkow, M. Muller, and Y.-H. Yang, “Theme transformer: Symbolic music generation with theme-conditioned transformer,” IEEE Transactions on Multimedia, 2022.
[23] Z. Wang, Y. Zhang, Y. Zhang, J. Jiang, R. Yang, J. Zhao, and G. Xia, “Pianotree vae: Structured representation learning for polyphonic music,” in Proceeding of the 21st International Society for Music Information Retrieval Conference, 2020.
[24] D. von Rütte, L. Biggio, Y. Kilcher, and T. Hofmann, “Figaro: Controllable music generation using learned and expert features,” in The Eleventh International Conference on Learning Representations, 2023.
[25] Y. Liao, W. Yue, Y. Jian, Z. Wang, Y. Gao, and C. Lu, “Micw: A multi-instrument music generation model based on the improved compound word,” in IEEE International Conference on Multimedia and Expo Workshops, pp. 1–10, IEEE, 2022.
[26] P. Gage, “A new algorithm for data compression,” The C Users Journal archive, vol. 12, pp. 23–38, 1994.
[27] 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), (Berlin, Germany), pp. 1715–1725, Association for Computational Linguistics, Aug. 2016.
[28] N. Fradet, N. Gutowski, F. Chhel, and J.-P. Briot, “Byte pair encoding for symbolic music,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 2001–2020, 2023.
[29] J. Tian, Z. Li, J. Li, and P. Wang, “N-gram unsupervised compoundation and feature injection for better symbolic music understanding,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 76
[30] X. Song, A. Salcianu, Y. Song, D. Dopson, and D. Zhou, “Fast WordPiece tokenization,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2089–2103, Nov. 2021.
[31] C. Raffel, Learning-based methods for comparing sequences, with applications to audio-to-MIDI alignment and matching. Phd thesis, Columbia University, Jan. 2016.
[32] A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, “The curious case of neural text degeneration,” in 8th International Conference on Learning Representations, 2020.
[33] A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret, “Transformers are rnns: Fast autoregressive transformers with linear attention,” in Proceedings of the 37th International Conference on Machine Learning, 2020.
[34] L.-C. Yang and A. Lerch, “On the evaluation of generative models in music,” Neural Computing and Applications, vol. 32, pp. 4773–4784, 2018.
[35] S.-L. Wu and Y.-H. Yang, “The jazz transformer on the front line: Exploring the shortcomings of aicomposed music through quantitative measures,” in Proceeding of the 21st International Society for Music Information Retrieval Conference, 2020.
[36] G. Mittal, J. H. Engel, C. Hawthorne, and I. Simon, “Symbolic music generation with diffusion models,” in Proceeding of the 22nd International Society for Music Information Retrieval Conference, pp. 468–475, 2021.
[37] Z. Qin, X. Han, W. Sun, D. Li, L. Kong, N. Barnes, and Y. Zhong, “The devil in linear transformer,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 7025–7041, Dec. 2022.