Understanding the science of music and then exploring the musical meaningfulness of such a scientific study is precisely what excites every scientific music researcher. This book, edited by David Meredith, gives a thorough exposition to most of the latest developments that have taken place in computational music analysis. The computational paradigm in music analysis, it must be mentioned, offers several advantages, for example, objectivity and the ability to process huge volumes of data.

The book has 17 chapters contributed by some of the leading music researchers, who have collectively done a terrific job in addressing as many topics as possible in this interdisciplinary area. The wide range of topics covered in the book includes, but is not limited to, information theory, music information retrieval (MIR), linguistics, pattern recognition, signal processing, machine learning, algebra, and topology. The chapters are split into six parts: “Methodology” (Part 1), “Chords and Pitch Class Sets” (Part 2), “Parsing Large-Scale Structure” (Part 3), “Grammars and Hierarchical Structure” (Part 4), “Motivic and Thematic Analysis” (Part 5), and “Classification and Distinctive Patterns” (Part 6).

Several of the techniques employed by these researchers, for example, Lerdahl and Jackendoff’s generative theory of tonal music and Forte’s pitch-class set theory, are already well established across the world among scientific researchers of music of different genres. On the minus side, I did not find a single article on raga analysis, despite Indian classical music’s acknowledged traditional richness. A comparative study between Indian classical music (ICM) and Western art music (WAM) would have given more variety and a sense of completeness to this otherwise excellent book. Nevertheless, I would strongly recommend this lucidly edited volume to all music researchers, as well as to students of music theory and analysis. It will also be useful to those interested in music technology.

As a final comment, I have personally found music analysis to be both challenging and fascinating. This is probably because understanding music both aesthetically and scientifically is only one aspect. The other is to interpret how music is actually understood, dwelling with music psychology and neuromusicology. And these two aspects are obviously not the same.