Constrained Markov Sequence Generation: Applications to Music and Text (Computational Synthesis and Creative Systems)

[François Pachet, Alexandre Papadopoulos, Pierre Roy] ✓ Constrained Markov Sequence Generation: Applications to Music and Text (Computational Synthesis and Creative Systems) ↠ Read Online eBook or Kindle ePUB. Constrained Markov Sequence Generation: Applications to Music and Text (Computational Synthesis and Creative Systems) This book deals with the problem of generating sequences in the style of a given corpus, in text or music. In particular, the authors study Markov models, which have long been used for capturing basic statistical information about how elements of a given alphabet are put together in representative sequences, formulating them as constraint satisfaction problems (CSPs), a formulation which opens the door to many extensions of basic Markov models through the modularity of constraint satisfaction.Th

Constrained Markov Sequence Generation: Applications to Music and Text (Computational Synthesis and Creative Systems)

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Rating : 4.54 (923 Votes)
Asin : 3319434969
Format Type : paperback
Number of Pages : 108 Pages
Publish Date : 2016-12-16
Language : English

DESCRIPTION:

In particular, the authors study Markov models, which have long been used for capturing basic statistical information about how elements of a given alphabet are put together in representative sequences, formulating them as constraint satisfaction problems (CSPs), a formulation which opens the door to many extensions of basic Markov models through the modularity of constraint satisfaction.The book will be valuable to practitioners, researchers, and graduate students engaged with algorithmic composition and constraint satisfaction.. From the Back CoverThis book deals with the problem of generating sequences in the style of a given corpus, in text or music

This book deals with the problem of generating sequences in the style of a given corpus, in text or music. In particular, the authors study Markov models, which have long been used for capturing basic statistical information about how elements of a given alphabet are put together in representative sequences, formulating them as constraint satisfaction problems (CSPs), a formulation which opens the door to many extensions of basic Markov models through the modularity of constraint satisfaction.The book will be valuable to practitioners, researchers, and graduate students engaged with algorithmic composition and constraint satisfaction.

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