LayerIt：面向时间对齐、可组合音乐可视化的框架
LayerIt: Towards a Framework for Time-Aligned, Composable Music Visualizations
AI总结：
提出LayerIt开源Python库，在共享表演时间轴上组合乐谱与信号表示，生成保留MEI结构的SVG，并通过节拍跟踪分析验证其可同时检查跟踪输出、信号和乐谱以定位错误。
AI中文摘要：
音乐信息检索通常需要在不同坐标系之间关联信号派生、算法和符号信息。现有可视化通常将乐谱与物理时间分离，而组合两者的复合可视化则需手工拼装。我们提出LayerIt，一个开源Python库，用于在共享表演时间轴上组合独立表示与乐谱。给定扭曲到表演时间的乐谱和音符级对齐，LayerIt生成单个SVG，保留乐谱的MEI结构，并保持添加组件的可识别性和可重样式化。我们通过一项具有挑战性的节拍跟踪分析进行演示，在该分析中，跟踪器输出、信号表示和乐谱可一起检查，以定位节拍不一致及其他错误，同时对照记谱结构和表演时间。
英文摘要：
Music information retrieval often relates signal-derived, algorithmic, and symbolic information across different coordinate systems. Existing visualizations typically leave notation separate from physical time, while composites that combine them are assembled by hand. We present LayerIt, an open-source Python library for composing independent representations with notation on a shared performance-time axis. Given a score warped into performance time and a note-level alignment, LayerIt emits a single SVG that preserves the score's MEI structure and keeps added components identifiable and restylable. We demonstrate this through a challenging beat-tracking analysis, in which tracker output, signal representations, and notation can be inspected together to locate metrical disagreement and other errors against both notated structure and performed time.
