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

Wei Zeng; Anvay Mathur; Ye Wang

cs.SDOct 26, 2026

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

Authors: Wei Zeng, Anvay Mathur, Ye Wang

Organizations: Integrative Sciences and Engineering Programme, NUS Graduate School · School of Computing, National University of Singapore · Singapore American School

Abstract

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 https://github.com/wei-zeng98/gangqinpu.

Figures & tables

Figure 1: Musical representations and associated tasks. Arrows indicate task input–output relationships; colors distinguish transcription ( red ), rendering ( blue ), and beat tracking ( green ).

		Size			Modalities			Origin	Annotations
Dataset	Genre	Pieces / Scores	Perf.	Hours	Audio	MIDI	Score	Audio / MIDI Source	Align.	Beats
MAESTRO v3 [ 31 ]	Mostly classical	–	1,276	198.7	$\checkmark$	$\checkmark$	–	Real / Captured	–	–
ASAP/(n)ASAP [ 29 , 34 ]	Classical	235	1,067	94.5	External △	$\checkmark$	$\checkmark$	Real / Captured	Beat / Note †	$\checkmark$
ATEPP v1.2 [ 32 ]	Classical	1,595	11,674	$\sim$ 1,000	External	$\checkmark$	$\triangle^{\ast}$	Real (web) / AMT	–	–
ACPAS [ 35 ]	Classical	497	2,189	179.8	Ext./Synth.	$\checkmark$	$\checkmark$	Real+Synth. / Mixed	Beat §	$\checkmark$
PianoCoRe-A [ 36 ]	Primarily classical	1,591	157,207	12,509	–	$\checkmark$	$\checkmark$	None / Mixed	Note	–

Table 1: Comparison of piano datasets by scale, modalities, data origin, and score–performance annotations.

Figure 2: Overview of the Gangqinpu dataset construction pipeline.

Feature	Definition
Timing variability	Standard deviation of local onset residuals.
Timing correlation	Lag-one autocorrelation of onset residuals.
Timing entropy	Shannon entropy of onset residuals discretized into 10 ms bins.
Chord asynchrony	Median onset range within groups of simultaneously notated notes.
Velocity variability	Standard deviation of note velocities.
Velocity diversity	Number of distinct note velocities.

Table 2: Score-informed timing and dynamic features used for expressive performance classification.

Figure 3: Distributions of (a) automatically assigned genre labels, (b) audio duration, (c) refined performance MIDI note counts, including interpolated notes, and (d) performances per score group. Panels (b) and (c) use logarithmic horizontal axes; panel (d) uses a logarithmic vertical axis. Dashed lines indicate medians.

Statistic	Value
Performances	18,786
Score groups	15,645
Groups with multiple performances	2,445
Total audio duration (hours)	1,007.1
Mean audio duration (min)	3.22
Median audio duration (min)	3.04

Table 3: Summary statistics of Gangqinpu. Score groups are identified automatically through score-similarity analysis.

Subset	$n$	Ours	SyMuPe	NOMML
ASAP (human)	246	100.0%	99.6%	100.0%
expressive	195	99.0%	86.7%	100.0%
non-expressive	93	97.8%	18.3%	0.0%
hard_neg (synthetic)	120	100.0%	100.0%	0.0%
noise 46 ms (synthetic)	120	98.3%	100.0%	0.0%
noise 60 ms (synthetic)	120	83.3%	100.0%	0.0%

Table 4: Comparison with SyMuPe and NOMML. Per-subset values are detection rates for that subset’s single ground-truth class; pooled rows combine the three real-data splits (

n{=}534

), the only ones mixing both classes.

Target	Training data	F1 $\uparrow$	CMLt $\uparrow$	AMLt $\uparrow$
ASAP test
Beat	ASAP	57.69	21.66	26.63
	ASAP + Gangqinpu	61.98	28.57	30.64
Downbeat	ASAP	43.23	10.90	21.30
	ASAP + Gangqinpu	47.55	13.41	22.79
Gangqinpu test

Table 5: Beat This! results under two training conditions. All metrics are expressed as percentages.

		ASAP test		Gangqinpu test
Prediction target	Metric $\uparrow$	ASAP	ASAP + G	ASAP	ASAP + G
Beat label	F1	62.97	64.89	69.28	83.30
Downbeat label	F1	43.36	48.68	42.02	62.97
Hand assignment	Macro-F1	86.65	84.81	86.99	93.99
Key signature	Macro-F1	36.26	30.87	78.39	79.33
Time signature	Macro-F1	45.40	43.96	51.00	45.43

Table 6: PM2S results under ASAP-only and combined training (ASAP + G, where G denotes Gangqinpu). All scores are percentages. Bold indicates the better training condition for each target within each test set.

Training data	Velocity $\downarrow$	IOI (ms) $\downarrow$	Duration (ms) $\downarrow$
ASAP test
ASAP	16.40	66.40	999.66
ASAP + Gangqinpu	16.62	68.96	1,058.28
Gangqinpu test
ASAP	17.50	78.51	309.47
ASAP + Gangqinpu	13.05	54.66	269.04

Table 7: Expressive performance rendering results using the adapted S2A model. All metrics are MAE. Velocity errors are in MIDI velocity units.

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Music Information Retrieval

Open paper AskPDF arXiv

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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