Editor’s note: Arachnophonia (“Arachno” = spider / “-phonia” = sound) is a regular feature on our blog where members of the UR community can share their thoughts about resources from the Parsons Music Library‘s collection.
All links included in these posts will take you to either the library catalog record for the item in question or to additional relevant information from around the web.
Today’s installment of Arachnophonia is by student assistant Christine (class of 2025) and features a book about mathematics in music. Thanks, Christine!
Mathematical Music: From Antiquity to Music AI by Nikita Braguinski
If you or a musician you know have ever learned a challenging piece, one of the most important things you can do to successfully practice is count as you play. Measures, rhythms, polyrhythms, fingerings, tempo… numbers are all over music, whether explicitly or hidden between the notes. What you may not realize is that the mathematics of music has been studied for thousands of years and is a widely expanding field today. In the book Mathematical Music: From Antiquity to AI, Nikita Braguinski explores this relationship from 550 B.C. to the present-day and future.
If this doesn’t sound interesting yet, here are a few fun facts from the book:
– The first machine entirely dedicated to “composing” music was designed around 1650 and combined random snippets of notes to generate a melody. Referred to as a “musical thinking machine”, this demonstrates just how long people have been using machines and mathematics to create music – hundreds of years!
– Some of the same names we know from calculus and other advanced math reappear on the music scene as well. Both Euler and Leibniz published works searching for the hidden mathematics behind what makes different ratios of frequencies (or intervals) delightful or unappealing to the human ear. Although they didn’t find anything concrete, they introduced the idea of listening as an art of subconscious counting.
– The (then) newly-formed Soviet Union had an intense interest in structural formalism in music and created multiple initiatives dedicated to art as a science. This coincided with an era of musical exploration into dissonant, atonal music and shows how the new revolutionaries distinguished themselves from the traditional Russian music of years past.
– Today, we have the computerized tool of neural networks, a deep learning AI technique to generate music on the spot given a certain style (or input parameter). Where do you think this will take music?
All of these stories, experiments, and techniques can be found in the Parsons Music Library. If you’re intrigued, be sure to check out this book along with others on the interdisciplinary nature of music.
