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

Jiyeon Bae, Jinwook Seo

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.

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

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.
Paper Structure (10 sections, 1 figure)

This paper contains 10 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Interactive Visualization
  3. Analytic Tasks
  4. Preprocessing
  5. Visual Encoding
  6. Artist--Decade Bubble Chart
  7. Quadrant Trajectory Map
  8. Song Table and Audio Profiles
  9. Expert Case Study and Discussion
  10. Limitations and Future Work

Figures (1)

  • Figure 1: Michael Jackson (1990s) and "Remember The Time" selected. (3) Song Performance Table listing charting metrics. (4) Audio Features Profile displaying quadrant classification, per-feature deviations from the era average, and a song-level radar overlay.