Author:
Carnovalini Filippo,Harley Nicholas,Homer Steven T.,Rodà Antonio,Wiggins Geraint A.
Publisher
Springer International Publishing
Reference48 articles.
1. Abdallah, S., Gold, N., Marsden, A.: Analysing symbolic music with probabilistic grammars. In: Computational Music Analysis, pp. 157–189. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25931-4_7
2. Anderson, C., Eigenfeldt, A., Pasquier, P.: The generative electronic dance music algorithmic system (GEDMAS). In: Proceedings of the Artificial Intelligence and Interactive Digital Entertainment (AIIDE’13) Conference, p. 4. AAAI Press, Boston, MA (2013)
3. Briot, J.P., Hadjeres, G., Pachet, F.D.: Deep Learning Techniques for Music Generation. Computational Synthesis and Creative Systems. Springer International Publishing, New York, NY (2020). https://doi.org/10.1007/978-3-319-70163-9, https://www.springer.com/gp/book/9783319701622
4. Briot, J.-P., Pachet, F.: Deep learning for music generation: challenges and directions. Neural Comput. Appl. 32(4), 981–993 (2018). https://doi.org/10.1007/s00521-018-3813-6
5. Cambouropoulos, E.: Towards a general computational theory of musical structure. Ph.D. thesis, Ph.D. thesis, University of Edinburgh (1998)