PianoCoRe: Combined and Refined Piano MIDI Dataset

Hugging Face Daily Papers Papers

Summary

PianoCoRe is a large-scale piano MIDI dataset unifying and refining open-source corpora with 250,046 performances of 5,625 pieces by 483 composers, featuring note-level alignments for music information retrieval and including a MIDI quality classifier and alignment refinement pipeline.

Symbolic music datasets with matched scores and performances are essential for many music information retrieval (MIR) tasks. Yet, existing resources often cover a narrow range of composers, lack performance variety, omit note-level alignments, or use inconsistent naming formats. This work presents PianoCoRe, a large-scale piano MIDI dataset that unifies and refines major open-source piano corpora. The dataset contains 250,046 performances of 5,625 pieces written by 483 composers, totaling 21,763 h of performed music. PianoCoRe is released in tiered subsets to support different applications: from large-scale analysis and pre-training (PianoCoRe-C and deduplicated PianoCoRe-B) to expressive performance modeling with note-level score alignment (PianoCoRe-A/A*). The note-aligned subset, PianoCoRe-A, provides the largest open-source collection of 157,207 performances aligned to 1,591 scores to date. In addition to the dataset, the contributions are: (1) a MIDI quality classifier for detecting corrupted and score-like transcriptions and (2) RAScoP, an alignment refinement pipeline that cleans temporal alignment errors and interpolates missing notes. The analysis shows that the refinement reduces temporal noise and eliminates tempo outliers. Moreover, an expressive performance rendering model trained on PianoCoRe demonstrates improved robustness to unseen pieces compared to models trained on raw or smaller datasets. PianoCoRe provides a ready-to-use foundation for the next generation of expressive piano performance research.
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Source: https://huggingface.co/papers/2605.06627

Abstract

A large-scale piano MIDI dataset called PianoCoRe is introduced, featuring unified and refined open-source corpora with diverse performances and note-level alignments for music information retrieval applications.

Symbolic music datasets with matched scores and performances are essential for manymusic information retrieval(MIR) tasks. Yet, existing resources often cover a narrow range of composers, lack performance variety, omitnote-level alignments, or use inconsistent naming formats. This work presents PianoCoRe, a large-scale pianoMIDIdataset that unifies and refines major open-source piano corpora. The dataset contains 250,046 performances of 5,625 pieces written by 483 composers, totaling 21,763 h of performed music. PianoCoRe is released in tiered subsets to support different applications: from large-scale analysis and pre-training (PianoCoRe-C and deduplicated PianoCoRe-B) to expressiveperformance modelingwith note-level score alignment (PianoCoRe-A/A*). The note-aligned subset, PianoCoRe-A, provides the largest open-source collection of 157,207 performances aligned to 1,591 scores to date. In addition to the dataset, the contributions are: (1) aMIDIquality classifier for detecting corrupted and score-like transcriptions and (2) RAScoP, analignment refinementpipeline that cleanstemporal alignment errorsand interpolates missing notes. The analysis shows that the refinement reduces temporal noise and eliminates tempo outliers. Moreover, anexpressive performance renderingmodel trained on PianoCoRe demonstrates improved robustness to unseen pieces compared to models trained on raw or smaller datasets. PianoCoRe provides a ready-to-use foundation for the next generation of expressive piano performance research.

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#### SyMuPe/Aria-MIDI-MLM Feature Extraction• Updatedabout 4 hours ago • 31 #### SyMuPe/MIDI-Quality-Classifier Updatedabout 4 hours ago • 30

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