cs.SDOct 11, 2026

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

Authors: Sihun Lee, Dasaem Jeong

Organizations: Sogang University Department of Artificial Intelligence Seoul, South Korea

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Abstract

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.

Figures & tables

Figure 1: An example illustration of the annotations in JSIR. The chord and structure labels are aligned to the beat positions.
DatasetBeatsChordsStructureSongsDuration
WJD [ 23 ]DD 34412.8 h
JSD [ 1 ]  D34033.4 h
JAAH [ 7 ]DDD1136.9 h
JSIRDDD26725.6 h
Table 1: Comparison between JSIR and three other jazz-focused datasets: three label types, number of recordings, and the duration of audio covered (in hours).
(a) Beat and downbeat tracking — Beat This!, 8-fold cross-validation; F-measure and CMLt in %
 Jazz test sets (211 tracks)JSIR (267 recordings)Non-jazz datasets
Training dataBeat FDownbeat FDownbeat CMLtBeat FDownbeat FDownbeat CMLtBeat FDownbeat F
16 datasets [ 8 ]93.487.779.094.686.572.789.086.2
16 datasets + JSIR93.689.281.497.190.681.188.986.0
Table 2: Baseline results for the three tasks. (a) Held-out jazz test sets are RWC-Jazz, JAAH, and Filosax; on JSIR the first row is zero-shot; non-jazz: the remaining training sets of Beat This! (4345 tracks, 3533 with downbeat annotations). (b) Cross-validated rows are means over the five fold models on JAAH and pooled held-out folds on JSIR; three overlapping tracks were excluded. † The 47 validation recordings (eight standards) of the hold-out split, on which the pop model reaches a root accuracy of 59.2. (c) HR: boundary hit-rate F-measure at 0.5 s and 3 s, subscript t: interior boundaries only; PWF: pairwise frame clustering; Accuracy: frame-level label accuracy. Bold marks the best value per column; the training-set-size rows use a different test subset and are left unmarked.
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