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1. Iñesta, J.M.; Conklin, D.; Ramírez, R.
   "Machine learning and music generation"
   Journal of Mathematics and Music, vol. 10, pp. 87--91 (2016)
   : bibtex : pdf : DOI
   Abstract:

   Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style.

@article {
 author = "Iñesta, J.M.; Conklin, D.; Ramírez, R.",
 title  = "Machine learning and music generation",
 issn = "1745-9737",
 journal = "Journal of Mathematics and Music",
 month = "October",
 number = "2",
 pages = "87--91",
 volume = "10",
 year = "2016"
}