SOMphony: Visualizing Symphonies Using Self Organizing Maps

Abstract

Symphonies are musical compositions played by a full orchestra which have evolved in style since the 16^th Century. Self-Organizing Maps (SOM) are shown to be useful in visualizing symphonies as a musical trajectory across the nodes in a trained map. This allows for some insights about the relationships and influences between and among composers in terms of their composition styles, and how the symphonic compositions have evolved over the years from one major music period to the next. The research focuses on Self Organizing Maps that are trained using 1-second music segments extracted from 45 different symphonies, from 15 different composers, with 3 composers from each of the 5 major musical periods. The trained SOM is further processed by doing a k-means clustering of the node vectors, which then allows for the quantitative comparison of music trajectories between symphonies of the same composer, between symphonies of different composers of the same music period, and between composers from different music periods.