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Music viewed by its entropy content: A novel window for comparative analysis.
Gerardo Febres1,2, Klaus Jaffe2
1Departamento de Procesos y Sistemas, Universidad Simón Bolívar, Sartenejas, Baruta, Miranda, Venezuela.
Plos One
|October 18, 2017
Summary
This study introduces a new method to analyze music using entropy and symbol frequency profiles, creating a unique "Fundamental Scale" for music representation. These techniques effectively classify music by type, style, and genre, with applications in pattern recognition.
Area of Science:
- Music Information Retrieval
- Information Theory
- Computational Musicology
Background:
- Traditional music analysis often relies on subjective interpretation or limited feature sets.
- Objective, quantitative methods are needed to capture the complexity of musical structures across genres and historical periods.
Purpose of the Study:
- To develop a novel framework for representing and analyzing polyphonic music using information theory concepts.
- To create a quantitative method for classifying music based on its symbolic structure and diversity.
Main Methods:
- Analysis of polyphonic music files using a "Fundamental Scale" derived from Minimal Entropy Description.
- Development of methods for scale adjustment, modeling symbol frequency profiles, and calculating "2nd Order Entropy" (diversity index).
- Application of these methods to diverse musical pieces to map their characteristics in a "symbolic specific diversity-entropy" space.
Main Results:
- The "symbolic specific diversity-entropy" and "2nd Order Entropy" spaces capture unique characteristics of music types, styles, composers, and genres.
- Clustering of musical properties observed within specific musical categories.
- Visualization of the historical trajectory of academic music from medieval to contemporary periods.
Conclusions:
- The developed entropy-based methods provide a robust way to characterize musical structures, including traditional and popular music.
- These techniques offer a powerful tool for music classification and have potential applications in broader pattern recognition and machine learning fields.
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