A Multi-Level Visual Analytics Approach to Artist-Era Alignment in Popular Music
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911537828986880 |
|---|---|
| author | Bae, Jiyeon Seo, Jinwook |
| author_facet | Bae, Jiyeon Seo, Jinwook |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21624 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Multi-Level Visual Analytics Approach to Artist-Era Alignment in Popular Music Bae, Jiyeon Seo, Jinwook Human-Computer Interaction 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. |
| title | A Multi-Level Visual Analytics Approach to Artist-Era Alignment in Popular Music |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.21624 |