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Gangqinpu: A Multi-Genre, Multi-Task Piano Dataset with Aligned Audio, MIDI, and Musical Scores — arXiv:2610.34694

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.34694
License (as archived): CC0-1.0
Archived: 2026-10-27T08:02:08.676Z
SHA-256 of the archived text: 91a59a771ff5cd2d525675033b0a0b2f5ed810001699d50c6c018a746a11d393
Stable URL of this page: https://metraipolis.com/documento/dc_648fa02029bc

How to cite

Gangqinpu: A Multi-Genre, Multi-Task Piano Dataset with Aligned Audio, MIDI, and Musical Scores — arXiv:2610.34694 — arXiv (en), https://arxiv.org/abs/2610.34694 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_648fa02029bc (Metraipolis archive dc_648fa02029bc, sha256 91a59a771ff5cd2d525675033b0a0b2f5ed810001699d50c6c018a746a11d393)

BibTeX

@misc{dc648fa02029bc,
  title = {Gangqinpu: A Multi-Genre, Multi-Task Piano Dataset with Aligned Audio, MIDI, and Musical Scores — arXiv:2610.34694},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.34694},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_648fa02029bc; sha256 91a59a771ff5cd2d525675033b0a0b2f5ed810001699d50c6c018a746a11d393},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.34694. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Aligned audio, performance MIDI, and musical scores provide valuable supervision for music analysis and performance modeling, yet datasets that jointly align all three modalities remain scarce and limited in scale and genre diversity. We introduce Gangqinpu, a multi-genre piano dataset containing 18,786 score-performance pairs and 1,007.1 hours of source audio, with automatic note-level alignments and derived beat/downbeat annotations. It spans diverse genres, including pop, classical, jazz, folk/traditional, rock, and blues. Our automated pipeline transcribes piano audio, aligns the resulting MIDI with musical scores, assigns genre labels, and filters out non-expressive performances with a score-informed binary classifier. We further construct joint splits with ASAP using conservative score-similarity grouping to reduce data leakage. We evaluate Gangqinpu on performance MIDI-to-score transcription, expressive performance rendering, and beat tracking. Downstream experiments demonstrate the value of Gangqinpu as complementary training data, with cross-dataset gains in beat tracking and primarily in-domain benefits for transcription and expressive performance rendering. The code and dataset are publicly available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Sound (cs.SD) ; Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS).
Pagina del lavoro: https://arxiv.org/abs/2610.34694 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-27T07:55:30.932Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).