Algorithmic Composition: Paradigms of Automated Music Generation

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Springer Science & Business Media, Aug 28, 2009 - Mathematics - 287 pages

Algorithmic composition – composing by means of formalizable methods – has a century old tradition not only in occidental music history. This is the first book to provide a detailed overview of prominent procedures of algorithmic composition in a pragmatic way rather than by treating formalizable aspects in single works. In addition to an historic overview, each chapter presents a specific class of algorithm in a compositional context by providing a general introduction to its development and theoretical basis and describes different musical applications. Each chapter outlines the strengths, weaknesses and possible aesthetical implications resulting from the application of the treated approaches. Topics covered are: markov models, generative grammars, transition networks, chaos and self-similarity, genetic algorithms, cellular automata, neural networks and artificial intelligence are covered. The comprehensive bibliography makes this work ideal for the musician and the researcher alike.


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I would like this prose a lot more if I could share it freely with people of like mind. Rights management is modern slavery. Don't do business with these sorts. Brilliant work. You do not need them.

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This book necessarily has a mixed content of music and mathematics, but the audience appears to be firmly students of algorithmic composition, and that includes beginning students. Despite its worrying problems with details (more later), students have a good basic text that will explain the fundamental ideas of style imitation in algorithmic composition. Although I find it short on real examples of interesting music, and perhaps long on superficial examples, the book does manage a broad overview of musical style imitation techniques and brings together a large range of disparate literature that has a bearing on the subject.
The introduction goes through the chapter layout of the book and discusses its intent. One of the limitations it takes on, and one that disappointed me, is that the works of individual composers, or individual works, are not dealt with.
I approached this book with a large degree of enthusiasm and optimism, unfortunately I left it feeling very disappointed. The emphasis on style imitation in the book and the lack of discussion of “genuine composition” is, I feel, a great shortcoming. There was an important opportunity here to go beyond style imitation—to me, as a composer, it is the least interesting or useful aspect of algorithmic composition—and delve into the more difficult realm of creative composition of compelling musical works. Furthermore, the book does not examine in detail how some composers have used algorithmic principles to create great works, let alone attack the core issue itself. While the systems outlined in the book may be applied in a range of ways from the superficial and trivial to the very sophisticated, nowhere is the problem addressed of what musical composition really is. This has been a problem with some other books on algorithmic style imitation, but to my mind, musical composition is something other than the application of a set of rules (particularly music theory rules), and it goes beyond this. Style imitation already has a large body of literature; I feel this book does a good job of pulling much of it together, but it has missed an opportunity to go further and add to the literature on algorithmic approaches to genuine composition.


Historical Development of Algorithmic Procedures
Markov Models
Generative Grammars
Transition Networks
Chaos and SelfSimilarity
Genetic Algorithms
Cellular Automata
Artificial Neural Networks
Artificial Intelligence
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