In the interest of full disclosure, I will preface this review with a disclaimer. Every chapter in this book is about something I care about. I found myself nodding along as I read. In a sense, this text was ‘preaching to the choir’ in my case.
All of the Oxford Handbooks tend to be weighty tomes, fairly comprehensive on a subject, with varying degrees of depth for each topic in the subject. This book is no different. McLean and Dean set out to represent the diversity of thought and practice under the umbrella of ‘algorithmic music’ by including chapters from authors who are as diverse as their ideas. They collected chapters that range from philosophy, to tools, to cultural practice, and everything in between. In this regard, McLean and Dean really have achieved what they set out to do. Anyone who has an interest in algorithmic music will find a chapter relevant to their own personal preoccupations and practice. If one tends to fall into intellectual rabbit holes, this book is a map of the warren entrances.
Many chapters are written by familiar and important figures in algorithmic music, including Warren Burt, Nick Collins, George Lewis, Carla Scaletti, Palle Dahlstedt, Christopher Haworth, Margaret Schedel and Mary Simoni, to name a few. With dozens of contributors, it would be ludicrous to list them all. But, in total, they represent independent artists, academics, inventors and more. They hail from a diversity of regions in North America, Europe and Australia. For this reason, the dearth of other nationalities, Asian contributors in particular, is a bit noticeable.
The book is separated into three parts. The first part, ‘Grounding Algorithmic Music’, provides multiple perspectives on what constitutes algorithmic music or even algorithms, the language and terminology used, and the communities and cultures that could be included by these definitions. In the second part, ‘What Can Algorithms in Music Do?’, the chapters could be broadly described as the tools of the trade. If the second part is the ‘how’, then the third part is the ‘why’. It presents a variety of ways in which algorithms are employed in music making. And the final part, ‘Algorithmic Culture’, contextualises algorithmic music practice in communities, cultures and societies.
Invoking my original disclaimer, I found that the book had many more strengths than weaknesses. One particular strength are the sections called ‘Perspectives on Practice’. Though many of the authors are from academic backgrounds or have academic connections, these sections provide arenas for musicians to discuss personal aspects to their art. A few specific accounts resonated very strongly for me, in particular. Spiegel distinguishes between music she feels she owns versus ‘music-like textures’ and suggests that is one reason for her ‘low’ compositional output. She demonstrates, through her own practice and self-judgement, an issue that many musicians working in algorithmic music face: ultimate musical ownership of the musical materials generated. Lewis writes compellingly about the philosophical and political consequences of improvising machines. In his iconic way, Lewis identifies underlying assumptions in Western computer music that inform practical and social hierarchies in the production of algorithmic music. The cinematic writing of Kaffe Matthews’s snapshot, time-jump memoir from cutting beats to sonic biking is a joy to see (read) in one’s head. Her life is the playing out of artistic and intellectual pursuits that springboard from one fascinating inquiry to the next, a characteristic that many of us would understand.
Another strength is the extensive bibliography for each chapter. Want to pursue some heady philosophy of time? Look at the bibliography in Julian Rohrhuber’s chapter. How about breaking into machine learning? Fiebrink and Caramiaux have four pages of references. Constraint programming, aesthetics and mathematics, sonification, game theory, spatialisation, music education, politics and so much more, all in relation to algorithmic music: here is the reference text you are looking for.
McLean and Dean do not attempt to assert a kind of teleological narrative of algorithmic music, either. They state in their introduction they selected authors with differing viewpoints. At the same time, the book is not organised by any opposing dialectics. So there are no false controversies invented, either. However, if one takes on the whole book, one can find underlying philosophical positions throughout, especially with regard to creative agency. Implicitly or explicitly, the role of the human (e.g., intervention, evaluation, judgement, dissemination) in algorithmic music-making is questioned, with different answers for different authors. Whether one looks at the distinction Scaletti makes between data sonification versus data-driven music, or the assertion by Lewis that we learn more about ourselves by developing improvising computers, or Spiegel’s self-deprecating judgement that much of her work is not compositional output, the implications lead to different conclusions.
In a way, the only failing of this text arises from its successes. Any handbook this comprehensive cannot offer the depth of each topic that one might hope for. A single chapter on evolutionary algorithms will not match an entire text dedicated to it, such as Miranda and Biles’s Evolutionary Computer Music (Reference Miranda and Biles2007), even if the chapter reports the most recent advances and breakthroughs.
In some cases, aiming at a general audience may have precluded a more convincing chapter. For example, in ‘Linking Sonic Aesthetics with Mathematical Theories’, Milne acknowledges that the generalisation of the mathematical theories to traditional pitch and rhythm structures does not prevent their application to more musical spaces. However, that naturally limits the degree to which the aesthetics discussed apply to contemporary practice.
Some chapters have to take debates for granted in order to make a point in the space available to them. Simoni’s chapter begs the question whether the composer-audience gap in contemporary music stems from a lack of understanding. It also begs the question whether an affective response to music is more desirable than a cognitive one. Regardless of these assumed premises, her experiments and their results are appetising food for thought.
In all of these examples, the problem is that a text attempting to provide the breadth of perspectives on a subject cannot provide the depth to make every chapter stand entirely on its own. I find it hard to fault the text for that, however. Rather, I see these lacunae as, well, seductive rabbit holes. The question of intended audience is, perhaps, a little more complicated in this book than other handbooks, as well. Because algorithmic music is both a subset and a superset of some interrelated musical pursuits, the background and specialisation of any single reader is not going to be sufficient to fully appreciate every chapter at the same level.
This is not an introductory text by any means, and I do not believe the Oxford Handbook series intends to be so, in general. If one is looking for a starter’s guide to programming computers for making music, there are several better texts (e.g., Manaris and Brown Reference Manaris and Brown2014; Miranda Reference Miranda2001; Simoni and Dannenberg Reference Simoni and Dannenberg2013). But, anyone who makes, studies or participates in music with computers will find this book a very useful reference.
