# JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings

arXiv: https://arxiv.org/abs/2610.15356 · HTML: https://mlsanity.com/paper/2610.15356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 11 Oct 2026.

## Builds on (most cited first, 10 of 10)

- [madmom: a new Python Audio and Music Signal Processing Library](https://mlsanity.com/paper/1605.07008/) (2016)
- [All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio](https://mlsanity.com/paper/2307.16425/) (2023)
- [Beat this! Accurate beat tracking without DBN postprocessing](https://mlsanity.com/paper/2407.21658/) (2024)
- [SongFormer: Scaling Music Structure Analysis with Heterogeneous Supervision](https://mlsanity.com/paper/2510.02797/) (2025)
- [Audio-based Musical Version Identification: Elements and Challenges](https://mlsanity.com/paper/2109.02472/) (2021)
- [A Fully Convolutional Deep Auditory Model for Musical Chord Recognition](https://mlsanity.com/paper/1612.05082/) (2016)
- [Robust Downbeat Tracking Using an Ensemble of Convolutional Networks](https://mlsanity.com/paper/1605.08396/) (2016)
- [From Discord to Harmony: Decomposed Consonance-based Training for Improved Audio Chord Estimation](https://mlsanity.com/paper/2509.01588/) (2025)
- [Adapting Meter Tracking Models to Latin American Music](https://mlsanity.com/paper/2304.07186/) (2023)
- [Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches](https://mlsanity.com/paper/2509.11241/) (2025)

## Abstract (arXiv)

Beat tracking, chord estimation, and structure analysis are staple tasks of music information retrieval. Their datasets are drawn largely from Western popular music, and jazz remains a repertoire on which state-of-the-art systems underperform. We present JSIR, the Jazz Standards dataset for Information Retrieval. Its 267 recordings of 44 jazz standards span 25.6 hours, 3.7 times the audio of JAAH, the closest existing resource. For each recording, we provide annotations for beat and downbeat positions, chords, and sections, all produced by a jazz musician and revised in several passes. In baseline experiments, models trained with JSIR also improve on other jazz datasets: the downbeat F-measure of a state-of-the-art beat tracker rises from 87.7% to 89.2%, and the seventh-chord accuracy of a pop-trained chord estimator on JAAH from 34% to 41%. We publicly release the annotations, the annotation tool used to create them, and a web application that displays them in sync with the audio.