Music Viewed by its Entropy Content: A Novel Window for Comparative Analysis

TL;DR

This paper introduces a novel entropy-based method to analyze and compare musical pieces by representing them in a new symbolic diversity-entropy space, revealing unique characteristics across genres and historical periods.

Contribution

It develops a new approach using minimal entropy descriptions and higher order entropy to characterize and classify music based on symbolic diversity and entropy measures.

Findings

Music types and styles cluster distinctly in the entropy space.

Historical trajectories of music can be visualized using these entropy measures.

The method effectively characterizes traditional and popular music expressions.

Abstract

Polyphonic music files were analyzed using the set of symbols that produced the Minimal Entropy Description which we call the Fundamental Scale. This allowed us to create a novel space to represent music pieces by developing: a) a method to adjust a description from its original scale of observation to a general scale, b) the concept of higher order entropy as the entropy associated to the deviations of a frequency ranked symbol profile from a perfect Zipf profile. We called this diversity index the "2nd Order Entropy". Applying these methods to a variety of musical pieces showed how the space of "symbolic specific diversity-entropy" and that of "2nd order entropy" captures characteristics that are unique to each music type, style, composer and genre. Some clustering of these properties around each musical category is shown. This method allows to visualize a historic trajectory of academic music across this space, from medieval to contemporary academic music. We show that description of musical structures using entropy and symbolic diversity allows to characterize traditional and popular expressions of music. These classification techniques promise to be useful in other disciplines for pattern recognition and machine learning, for example.