# TimeCues Studio: A Workspace for Music Annotation and Algorithm Prototyping

arXiv: https://arxiv.org/abs/2609.10338 · HTML: https://mlsanity.com/paper/2609.10338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [Music Source Separation in the Waveform Domain](https://mlsanity.com/paper/1911.13254/) (2019)
- [madmom: a new Python Audio and Music Signal Processing Library](https://mlsanity.com/paper/1605.07008/) (2016)
- [A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation](https://mlsanity.com/paper/2203.09893/) (2022)
- [All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio](https://mlsanity.com/paper/2307.16425/) (2023)
- [SongFormer: Scaling Music Structure Analysis with Heterogeneous Supervision](https://mlsanity.com/paper/2510.02797/) (2025)
- [BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter Tracking](https://mlsanity.com/paper/2108.03576/) (2021)
- [Uncovering audio patterns in music with Nonnegative Tucker Decomposition for structural segmentation](https://mlsanity.com/paper/2104.08580/) (2021)
- [audino: A Modern Annotation Tool for Audio and Speech](https://mlsanity.com/paper/2006.05236/) (2020)
- [Towards Human-in-the-Loop Onset Detection: A Transfer Learning Approach for Maracatu](https://mlsanity.com/paper/2507.04858/) (2025)

## Abstract (arXiv)

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.