Music Viewed by its Entropy Content: A Novel Window for Comparative Analysis
This incremental work provides a new method for music analysis and classification, potentially useful for pattern recognition and machine learning in other disciplines.
The authors tackled the problem of analyzing and comparing polyphonic music by developing a novel representation space based on minimal entropy descriptions and higher-order entropy, which successfully captured unique characteristics of music types, styles, composers, and genres, with clustering shown around musical categories.
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.