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arXiv · 2609.10338

TimeCues Studio: A Workspace for Music Annotation and Algorithm Prototyping

Sapir Caduri·Yoav Goldberg
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Abstract

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.

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Sapir Caduri, Yoav Goldberg. 2026-09-09. TimeCues Studio: A Workspace for Music Annotation and Algorithm Prototyping. https://doi.org/10.1145/3767308.3834750

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