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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.
List of contents
Introduction.- Part I - Results.- Markov Constraints: Steerable Generation of Variable-Order Markovian Sequences.- Unary Constraints.- Meter.- Meter with Allen Relations.- Max Order and Plagiarism.- 1/ f Spectrum Sequences.- Regular Constraints.- Binary Equality Constraints.- Palindromes and N -Grams.- Part II - Applications and Examples.- Generating Lyrics.- Harmonization Using Unary Constraints.- Generation of Lead Sheets.- Conclusions.- Glossary.- Index.- References.
Summary
Deals with the problem of generating sequences in the style of a given corpus, in text or music
Overcomes limitation of Markov models, they capture only local information, by formulating them as constraint satisfaction problems (CSPs)
Valuable for practitioners, researchers, and graduate students engaged with algorithmic composition and constraint satisfaction
