+ General Information
+ Members
+ Research
+ Intranet

PRAIg '17 / +pics


AERFAI Summer School on Deep Learning


Alicante July 5-6, 2017

10th international workshop on Machine Learning and Music


Barcelona: 6th October 2017



  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

    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"
Resources associated with this publication
Valid XHTML 1.0!Valid CSS!