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2026
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A Multi-Source Pipeline for Extracting Traditional-Style Chinese Melody Data from Symbolic Files and Score Images
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Abstract
Large-scale symbolic melody datasets are essential for data-driven music information retrieval and generation, yet traditional-style Chinese melodies remain scattered across heterogeneous score formats and image sources. Existing extraction pipelines typically focus on single modalities-either MIDI archives or standard staff notation-and lack unified handling for numbered musical notation (Jianpu) and automated quality assurance. We propose the Multi-Source Melody Pipeline (MSMP), a systems-integration prototype whose front-end admits MIDI, MusicXML, Jianpu images, and staff images, and whose back-end converges on a standardized event-level representation; the present case study exercises the image branch-in particular the Jianpu branch, through a Gemini-2.5-flash vision language model-and treats the MIDI/MusicXML ingestion paths as architectural slots that are wired in but not experimentally validated in this submission. The system employs notation-aware routing to direct score images to appropriate backends (a VLM for Jianpu and rule-based OMR for staff) and enforces a structural validity gate (schema conformance plus at least one melodic track with at least one musical event) on every candidate segment. Validation on a 292-page representative prototype cohort yielded an 80.1% structural-acceptance rate-explicitly not a transcription accuracy number-and a newly added ground-truth benchmark on 50 manually annotated Jianpu pages reports 95.8% time-signature exact accuracy, 77.1% tonal-pitch-class key accuracy, 100% tempo agreement within ±5 BPM, and, on a 10-page note-level subset, a mean first-16-note pitch F1 of 0.898 (octave-sensitive) with a Symbol Error Rate of 0.150. A companion 10-page K = 3 self-consistency audit indicates that metadata errors are systematic rather than stochastic. This work, therefore, contributes a reproducible integration architecture and a quantitative baseline on the Jianpu branch, rather than a new OMR algorithm, a new dataset release, or a fully benchmarked multi-format corpus; ongoing work addresses out-of-distribution classifier evaluation, comparison against dedicated Jianpu OMR baselines, and release of a copyright-cleared corpus.
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