Algorithmic Composition: Paradigms of Automated Music Generation (review)

There is little doubt that algorithmic composition really came into its own as computers became less expensive and more accessible. Formalizable and mathematical approaches to musical composition have had a longer history, but were less intensely applied before the development of commodity computers.

Gerard Nierhaus is a composer who teaches Algorithmic Composition and Computer Music at the Institute of Electronic Music and Acoustics (IEM), University of Music and Dramatic Arts, in Graz, Austria.

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. When I checked, the publisher had the book listed in its mathematics section, which is misplaced, I think, as the level of mathematics in the book is really only what a music student would desire. Despite its worrying problems with details (more later), in this book 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. The book presents an overview of common or prominent methods of algorithmic composition in a systematic way, discussing their features, particularly for style imitation. After a rather extended historical overview, the chapters each discuss Markov models, generative grammars, transition networks, chaos and self-similarity (fractals), genetic algorithms, cellular automata, neural networks, and artificial intelligence.

The historical overview goes back to the Renaissance and further, discussing the development of philosophy, the abstraction and representation of numbers and counting in different cultures (back to about 3000 BCE), and so on. Although the development of algorithmic thinking may depend on these being in place, I found the chapter overlong and the details and level of treatment boring and mostly irrelevant to the immediate goal. It may, however, be fascinating to students, and it certainly provides key developments and the names of those involved. The discussion on calculating machines is quite thorough, covering developments by John Napier, Blaise Pascal, and more, through to the handheld mechanical calculators of the early 20th century and including Charles Babbage’s analytical engine. The history of modern mathematics continues, discussing key ideas about the formalization of logic and the development of the modern computer. The historical discussion of computers seems rather idiosyncratic, initially, as it does not define the requirements of such a machine, and secondly as it claims that Konrad Zuse constructed the first computer. Modern computer history texts (for example, Electronic Brains: Stories from the Dawn of the Computer Age by Mike Hally, and others) say that Zuse created one of the first electromechanical digital calculators, and the distinction is significant because the definition of what exactly constitutes a computer is important. This book fails to notice the distinction, and many other aspects of computer history, and also manages to misspell the names of several key people or give them incorrect titles. The subsequent discussion on what can be computed is somewhat confused but the general model of a computer is useful. The chapter ends by introducing the computer in algorithmic composition and briefly mentions the work of Lejaren Hiller and Leonard Isaacson, Max Mathews, and Iannis Xenakis.

Each of the following chapters adheres to a similar structure: some history, the underlying theory including some basic mathematics, then a more detailed discussion citing examples, and a summary to close the chapter. This makes it...