Decoding Musical Evolution Through Network Science

2025-01-13

AI Summary

This study investigates the diachronic evolution of musical complexity and its relationship with technological development. Using a corpus of approximately 20,000 MIDI pieces spanning the 17th–20th centuries, we construct weighted directed note networks and systematically compute graph-theoretic metrics—including degree distribution, clustering coefficient, and characteristic path length—to quantify structural complexity and melodic diversity. Our key findings are: (1) overall musical complexity exhibits a statistically significant long-term decline; (2) classical and jazz genres historically ranked highest in complexity but have undergone sustained simplification over the past century, converging toward the lower complexity levels characteristic of pop and electronic music; (3) the proliferation of digital platforms correlates with accelerated stylistic homogenization, supporting the technology-driven simplification hypothesis. This work pioneers the integration of network science into large-scale, cross-centennial music analysis, establishing a reproducible, quantitative framework for studying the interplay between artistic evolution and technological intervention.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Music has always been central to human culture, reflecting and shaping traditions, emotions, and societal changes. Technological advancements have transformed how music is created and consumed, influencing tastes and the music itself. In this study, we use Network Science to analyze musical complexity. Drawing on $approx20,000$ MIDI files across six macro-genres spanning nearly four centuries, we represent each composition as a weighted directed network to study its structural properties. Our results show that Classical and Jazz compositions have higher complexity and melodic diversity than recently developed genres. However, a temporal analysis reveals a trend toward simplification, with even Classical and Jazz nearing the complexity levels of modern genres. This study highlights how digital tools and streaming platforms shape musical evolution, fostering new genres while driving homogenization and simplicity.

Problem

• Music Complexity
• Time Evolution
• Technology Impact

Innovation

• Network Science
• Music Complexity Analysis
• Digital Tool Impact on Music Evolution