References
- 1Barbieri, G., Pachet, F., Roy, P., and Esposti, M. D. (2012).
Markov constraints for generating lyrics with style . In Proceedings of the European Conference on Artificial Intelligence, pages 115–120. IOS Press. - 2Bodily, P. M. and Ventura, D. (2021). Inferring structural constraints in musical sequences via multiple self-alignment. In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 43.
- 3Briot, J.-P. and Pachet, F. (2020). Deep learning for music generation: Challenges and directions. Neural Computing and Applications, 32(4):981–993. DOI: 10.1007/978-3-319-70163-9
- 4Collins, T. and Laney, R. (2017). Computer–generated stylistic compositions with long–term repetitive and phrasal structure. Journal of Creative Music Systems, 1(2). DOI: 10.5920/JCMS.2017.02
- 5Colton, S., Gow, J., Torres, P., and Cairns, P. A. (2010). Experiments in objet trouvé browsing. In Proceedings of the International Conference on Computational Creativity, pages 238–247.
- 6Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., and Liu, R. (2019). Plug and play language models: A simple approach to controlled text generation. arXiv preprint arXiv:1912.02164.
- 7Davies, M. (2009). The 385+ million word corpus of contemporary American English (1990–2008+): Design, architecture, and linguistic insights. International Journal of Corpus Linguistics, 14(2):159–190. DOI: 10.1075/ijcl.14.2.02dav
- 8Hadjeres, G. and Nielsen, F. (2020). AnticipationRNN: Enforcing unary constraints in sequence generation, with application to interactive music generation. Neural Computing and Applications, 32(4):995–1005. DOI: 10.1007/s00521-018-3868-4
- 9Huang, C.-Z. A., Vaswani, A., Uszkoreit, J., Shazeer, N., Hawthorne, C., Dai, A., Hoffman, M., and Eck, D. (2018). Music Transformer: Generating music with long-term structure (2018). arXiv preprint arXiv:1809.04281.
- 10Louie, R., Cohen, A., Huang, C.-Z. A., Terry, M., and Cai, C. J. (2020). Cococo: AI-steering tools for music novices co-creating with generative models. In 25th International Conference on Intelligent User Interfaces, Workshop on Human-AI Co-Creation with Generative Models.
- 11Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., and McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Proceedings of the Association for Computational Linguistics (System Demonstrations), pages55–60. DOI: 10.3115/v1/P14-5010
- 12Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26, pages 3111–3119.
- 13Nunes, J. C., Ordanini, A., and Valsesia, F. (2015). The power of repetition: Repetitive lyrics in a song increase processing fluency and drive market success. Journal of Consumer Psychology, 25(2):187–199. DOI: 10.1016/j.jcps.2014.12.004
- 14Oore, S., Simon, I., Dieleman, S., Eck, D., and Simonyan, K. (2020). This time with feeling: Learning expressive musical performance. Neural Computing and Applications, 32(4):955–967. DOI: 10.1007/s00521-018-3758-9
- 15Pachet, F., Paris, S. C., Papadopoulos, A., and Roy, P. (2017). Sampling variations of sequences for structured music generation. In Proceedings of the International Society for Music Information Retrieval Conference, pages167–173. DOI: 10.4324/9781315621364-19
- 16Pachet, F., Roy, P., Barbieri, G., and Paris, S. C. (2011). Finite-length Markov processes with constraints. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 635–642.
- 17Papadopoulos, A., Pachet, F., Roy, P., and Sakellariou, J. (2015).
Exact sampling for regular and Markov constraints with belief propagation . In Proceedings of the International Conference on Principles and Practice of Constraint Programming, pages341–350. Springer. DOI: 10.1007/978-3-319-23219-5_24 - 18Papadopoulos, A., Roy, P., and Pachet, F. (2014). Avoiding plagiarism in Markov sequence generation. In Proceedings of the Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence, pages 2731–2737.
- 19Perez, G. and Régin, J.-C. (2017).
MDDs: Sampling and probability constraints . In International Conference on Principles and Practice of Constraint Programming, pages226–242. Springer. DOI: 10.1007/978-3-319-66158-2_15 - 20Schulze, W. and Van Der Merwe, B. (2011). Music generation with Markov models. IEEE Annals of the History of Computing, 18(03):78–85. DOI: 10.1109/MMUL.2010.44
- 21Walker, W., Lamere, P., Kwok, P., Raj, B., Singh, R., Gouvea, E., Wolf, P., and Woelfel, J. (2004).
Sphinx-4: A flexible open source framework for speech recognition . Technical report, Sun Microsystems, Inc. - 22Widmer, G. (2016). Getting closer to the essence of music: The Con Espressione manifesto. ACM Transactions on Intelligent Systems and Technology (TIST), 8(2):1–13. DOI: 10.1145/2899004