Machine learning helps retrace evolution of classical music

This data visualization based on the researchers' model shows how four different musical modes - indicated by red, green, purple, and blue - predominated during the Renaissance. DCML / EPFL - Researchers in EPFL's Digital and Cognitive Musicology Lab in the College of Humanities used an unsupervised machine learning model to 'listen to' and categorize more than 13,000 pieces of Western classical music, revealing how modes - such as major and minor - have changed throughout history. Many people may not be able to define what a minor mode is in music, but most would almost certainly recognize a piece played in a minor key. That's because we intuitively differentiate the set of notes belonging to the minor scale - which tend to sound dark, tense, or sad - from those in the major scale, which more often connote happiness, strength, or lightness. But throughout history, there have been periods when multiple other modes were used in addition to major and minor - or when no clear separation between modes could be found at all. Understanding and visualizing these differences over time is what Digital and Cognitive Musicology Lab (DCML) researchers Daniel Harasim, Fabian Moss, Matthias Ramirez, and Martin Rohrmeier set out to do in a recent study, which has been published in the open-access journal Humanities and Social Sciences Communications . For their research, they developed a machine learning model to analyze more than 13,000 pieces of music from the 15^th to the 19^th centuries, spanning the Renaissance, Baroque, Classical, early Romantic, and late Romantic musical periods.