# JazzSAMBA: A Synchronous and Asynchronous Multi-take Band Audio Dataset of Jazz Standards for Live Music Models

arXiv: https://arxiv.org/abs/2609.34931 · HTML: https://mlsanity.com/paper/2609.34931/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 16 of 16)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity](https://mlsanity.com/paper/1909.08494/) (2019)
- [Moisesdb: A dataset for source separation beyond 4-stems](https://mlsanity.com/paper/2307.15913/) (2023)
- [Music Source Separation with Band-Split RoPE Transformer](https://mlsanity.com/paper/2309.02612/) (2023)
- [Anticipatory Music Transformer](https://mlsanity.com/paper/2306.08620/) (2023)
- [Beat this! Accurate beat tracking without DBN postprocessing](https://mlsanity.com/paper/2407.21658/) (2024)
- [RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning](https://mlsanity.com/paper/2002.03082/) (2020)
- [COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations](https://mlsanity.com/paper/2404.16969/) (2024)
- [ReaLJam: Real-Time Human-AI Music Jamming with Reinforcement Learning-Tuned Transformers](https://mlsanity.com/paper/2502.21267/) (2025)
- [Adaptive Accompaniment with ReaLchords](https://mlsanity.com/paper/2506.14723/) (2025)
- [Streaming Generation for Music Accompaniment](https://mlsanity.com/paper/2510.22105/) (2025)
- [JAZZVAR: A Dataset of Variations found within Solo Piano Performances of Jazz Standards for Music Overpainting](https://mlsanity.com/paper/2307.09670/) (2023)
- [LiveBand: Live Accompaniment Generation in the Audio Domain](https://mlsanity.com/paper/2606.03803/) (2026)
- [MuScriptor: An Open Model for Multi-Instrument Music Transcription](https://mlsanity.com/paper/2607.08168/) (2026)
- [MusPyExpress: Extending MusPy with Enhanced Expression Text Support](https://mlsanity.com/paper/2608.21678/) (2026)

## Abstract (arXiv)

Machine learning has made strong progress on music tasks, both as assistive tools and as creative partners. However, most systems train on multitrack corpora that emphasize pop and rock. Jazz, with improvisation at the core of its practice, still lacks a well-annotated corpus of clean per-stem combo recordings on standards. We introduce JazzSAMBA (Jazz Synchronous and Asynchronous Multi-take Band Audio) to fill this gap: the first originally recorded jazz-combo multitrack dataset of standards with asynchronous (overdubbed) and synchronous (live ensemble) protocols, preferred and alternate takes chosen by the musicians, and timed annotations for bars, chords, sections, and soloists. JazzSAMBA covers 76 standards by eight musicians on drums, bass, piano, trumpet, and saxophone, with per-stem audio, mixtures, and MIDI. It can support chart-conditioned accompaniment, combo source separation, and form-aware music information retrieval. We demonstrate the dataset on two tasks: a jazz combo source-separation baseline and a chart-conditioned accompaniment ablation. The dataset, code, and samples are linked from the project demo page.