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

TL;DR

This paper presents an interactive visual analytics framework for analyzing artist alignment with historical stylistic trends in popular music, using artist-epoch profiles and Spotify features to reveal stylistic conformity and divergence over decades.

Contribution

It introduces a novel multi-level visual analytics approach that models artist-epoch alignment relative to era-specific baselines using profile shape and contrast dimensions.

Findings

Artists show diverse stylistic trajectories over decades.

Alignment and intensity can diverge independently.

The framework reveals nuanced stylistic shifts in top-chart artists.

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.