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.

翻译：现有流行音乐计算研究主要建模整体趋势或预测榜单表现，对解读艺术家与历史风格基线对齐关系的支撑有限。我们提出一个交互式可视化分析框架，将每个艺术家-十年作为相对特定时代基线的定义单元，沿两个互补维度进行表征：轮廓形状相似度（捕捉与时代特征模式的方向对应性）和轮廓对比度（捕捉相对于时代离散度的风格强度）。这两个维度共同定义了基于象限的轨迹空间，用于推理艺术家随时间的趋同、背离与强化过程。将该框架应用于1960s-2010s六个十年间美国公告牌百强单曲榜历史前十艺术家每周上榜曲目（关联Spotify音频特征），框架揭示艺术家轨迹中对齐度与强度可能出现有意义的背离。