Problem
Research questions and friction points this paper is trying to address.
Jazz
Multitrack dataset
Music information retrieval
Source separation
Improvisation
JazzSAMBA: A Synchronous and Asynchronous Multi-take Band Audio Dataset of Jazz Standards for Live Music Models
🤖 AI Summary
This study addresses the scarcity of high-quality, fine-grained annotated multi-track raw recording datasets in the jazz domain by constructing the first jazz multi-track dataset featuring chord- and section-level temporal annotations. Methodologically, it introduces a novel acquisition protocol that integrates real-time ensemble performance with overdubbing, combining multi-track audio recording, MIDI transcription, and temporally aligned annotation techniques while providing musician-preferred versions. The resulting dataset comprises 76 standard jazz compositions. Its effectiveness is validated through downstream tasks, including music source separation and conditional accompaniment generation. This work establishes a significant benchmark for computational research in jazz, offering researchers a rigorously curated resource to advance studies in music information retrieval and AI-driven musical analysis within this complex genre.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Jazz multitrack dataset
Source separation
Chart-conditioned accompaniment
Synchronous and asynchronous recording
Music information retrieval
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