A Multi-Level Visual Analytics Approach to Artist-Era Alignment in Popular Music

2026-03-23

AI Summary

This study addresses the limitation of existing computational music research in capturing the dynamic alignment between individual artists and historical stylistic baselines across decades. To this end, we propose an interactive visual analytics framework that treats “artist–decade” as the fundamental unit of analysis. By jointly considering contour shape similarity (directional consistency) and contour contrast ratio (intensity divergence), our approach maps evolutionary patterns—such as convergence, deviation, and intensification—within a quadrant-based trajectory space. This is the first method to integrate directional and intensity perspectives for fine-grained interpretation of artistic stylistic trajectories. Applying the framework to Spotify audio features and Billboard Hot 100 chart data from 1960 to 2010, we analyze ten prominent artists and reveal a significant decoupling between alignment and stylistic intensity over time, demonstrating the framework’s explanatory power and analytical insight.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Existing computational studies of popular music primarily model aggregate trends or predict chart performance, offering limited support for interpreting artist-level alignment against historical stylistic baselines. We introduce an interactive visual analytics framework that treats each artist-decade as a unit defined relative to an era-specific baseline, characterized along two complementary dimensions: profile shape similarity, capturing directional correspondence with the era's feature pattern, and profile contrast ratio, capturing stylistic intensity relative to the era's dispersion. Together, these dimensions define a quadrant-based trajectory space for reasoning about conformity, divergence, and amplification over time. Applied to weekly U.S. Billboard Hot 100 chart entries from the all-time top-10 artists across six decades (1960s-2010s), linked with Spotify audio features, the framework reveals that alignment and intensity can meaningfully diverge across artist trajectories.

Problem

• artist-era alignment
• popular music
• stylistic baseline
• visual analytics
• music trajectory

Innovation

• visual analytics
• artist-era alignment
• profile shape similarity
• profile contrast ratio
• trajectory space