Editing Algorithmic composition (section)

===Systems that learn===
{{Further|Machine improvisation|Machine listening|Machine learning}}
Learning systems are programs that have no given knowledge of the genre of music they are working with. Instead, they collect the learning material by themselves from the example material supplied by the user or programmer. The material is then processed into a piece of music similar to the example material. This method of algorithmic composition is strongly linked to algorithmic modeling of style,<ref>S. Dubnov, G. Assayag, O. Lartillot, G. Bejerano, "[http://cms2.unige.ch/fapse/neuroemo/pdf/JournalDubnov.pdf Using Machine-Learning Methods for Musical Style Modeling] {{Webarchive|url=https://web.archive.org/web/20170810025000/http://cms2.unige.ch/fapse/neuroemo/pdf/JournalDubnov.pdf |date=2017-08-10 }}", ''IEEE Computers'', 36 (10), pp. 73–80, October 2003.</ref> [[machine improvisation]], and such studies as [[cognitive science]] and the study of [[Neural network (machine learning)|neural networks]]. Assayag and Dubnov<ref>G. Assayag, S. Dubnov, O. Delerue, "[http://articles.ircam.fr/textes/Assayag99a/index.pdf Guessing the Composer's Mind : Applying Universal Prediction to Musical Style]", in ''Proceedings of International Computer Music Conference'', Beijing, 1999.</ref> proposed a variable length [[Markov model]] to learn motif and phrase continuations of different length. Marchini and Purwins<ref>{{cite book|chapter-url=https://link.springer.com/chapter/10.1007%2F978-3-642-23126-1_14 |last1=Marchini |first1=Marco |last2=Purwins |first2=Hendrik|chapter=Unsupervised Analysis and Generation of Audio Percussion Sequences |title=Exploring Music Contents |date=2011 |volume=6684 |pages=205–218 |doi=10.1007/978-3-642-23126-1_14|series=Lecture Notes in Computer Science |isbn=978-3-642-23125-4}}</ref> presented a system that learns the structure of an audio recording of a rhythmical percussion fragment using unsupervised clustering and variable length Markov chains and that synthesizes musical variations from it.