发表机构
Bar-Ilan University（巴伊兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TimeCues Studio是一个开源工作空间，支持团队对音乐语料库进行歧义感知标注、比较检测算法并原型化新算法，通过统一时间线集成标注与开发，采用MIT许可并支持Docker Compose部署。
AI 中文摘要
多媒体应用需要精确的音乐标注——标记位置、片段或循环——这些标注可由人工或算法完成。机器学习算法具有可扩展性和有效性，但需要带标注的训练数据，而许多任务中这类数据稀缺。TimeCues Studio是一个开源工作空间，算法开发团队可在此对音乐语料库进行标注、将检测算法与这些标注进行比较，并原型化新算法。与现有每次仅针对单首曲目构建的工具不同，TimeCues面向标注整个音乐集合的团队，并与算法开发紧密集成。标注者可在网格锁定的时间线上放置多种标记类型——每种类型均支持歧义感知标注——该时间线可可视化多种音乐特征，包括分离的音频音轨。同一时间线驱动一个带有内置基线的算法比较引擎、一个用于原型化新模型的Python沙盒，以及一个尊重结构化字段的歧义感知评估器。同样的可视化也适用于音乐同步项目中的单人标注者。TimeCues采用MIT许可证，可通过一条Docker Compose命令部署。
英文摘要
Multimedia applications require precise music annotation-labeled positions, segments, or loops-placed by hand or algorithmically. Machine-learning algorithms are scalable and effective but need annotated training data, scarce for many tasks. TimeCues Studio is an open-source workspace where algorithm-development teams annotate a music corpus, compare detection algorithms against those annotations, and prototype new ones. Unlike existing tools built for a single track at a time, TimeCues targets teams annotating whole collections, tightly integrated with algorithm development. Annotators place several marker types-each supporting ambiguity-aware labeling-on a grid-locked timeline that visualizes many music features, including separated audio stems. The same timeline drives an algorithm-comparison engine with bundled baselines, a Python sandbox for prototyping new models, and an ambiguity-aware evaluator that honors the structured fields. The same visualization suits solo annotators on music-sync projects. TimeCues is MIT-licensed and deploys via one Docker Compose command.
Comments8 pages, 2 figures, to appear in Proceedings of the 34th ACM International Conference on Multimedia (MM '26)
