Aligned Classical Piano Audio and Score (ACPAS) dataset

ACPAS is a dataset with aigned audio and scores for classical piano music. For each performance, we provide the corresponding performance audio (real recording or synthesized), performance MIDI, and MIDI score, together with rhythm and key annotations.

The dataset data is partly collected from a list of Automatic Music Transcription (AMT) datasets, and partly synthesized using Native Instrument Kontakt Player.

List of source AMT datasets:

• MIDI Aligned Piano Sounds (MAPS) dataset
• A-MAPS dataset
• Classical Piano MIDI (CPM) dataset
• Aligned Scores and Performances (ASAP) dataset

In this document, we describe a piece of music composition as a music piece, a version of music score to the composition as a music score, and a music performance to a specific music score as a music performance. Thus, one music piece can have multiple versions of music scores (due to e.g. different repeats), and one music score can be mapped to multiple versions of music performances.

Due to different sources of data, the performances can be human performances (from the ASAP dataset) or hand-crafted tempos and dynamics to sound like human performances (from MAPS dataset and Classical Piano MIDI Page).

Dataset Contents

ACPAS dataset is composed of a Real recording subset and a Synthetic subset.

Real recording subset

This subset covers performances with real recordings from the MAPS "ENSTDkCl" and "ENSTDkAm" subsets and the MAESTRO dataset, in total 578 performances. The corresponding MIDI scores and annotations are from the A-MAPS dataset and the ASAP dataset.

Synthetic subset

This subset covers performances with synthetic audios from the following three sources, in total 1611 performances:

1. Performance audio and MIDI from the MAPS synthetic subsets, and MIDI score from the A-MAPS dataset.
2. MIDI performance and score from the ASAP dataset, and audio files synthesized from performance MIDIs using Native Instrument Kontakt Player.
3. MIDI performance and score from the CPM dataset, and audio files synthesized from performance MIDIs using Native Instrument Kontakt Player.

We make use of four different piano models provided in the Native Instrument Kontakt Player, and tune the piano font to be soft or hard. This end up with 8 different piano fonts. During synthesis, we add some level of reverberation to simulate real recordings. We reserve one piano model (both soft and hard fonts) to only be used for testing only. The other three piano models (6 fonts) are used for both training/validaton and testing sets.

Dataset Statistics

The dataset is splitted into train/validation/test in a way that there is no overlap between pieces over the whole dataset. To keep in line with the MAPS and MAESTRO train/test split, we reserve all the MAPS real recording pieces and the testing pieces in MAESTRO-v2.0.0 to the test split. However, the training/validation split is randomly selected.

Below are the basic statistics of this dataset:

Subset	Source	Split	Distinct Pieces	Performances	Duration (hours)
Real recording	MAPS	test	52	59	4.277917
Real recording	ASAP	train	109	368	32.737423
Real recording	ASAP	validation	17	49	2.524508
Real recording	ASAP	test	44	102	9.420974
Real recording	Both	Total	215	578	48.960822
Synthetic	--	train	359	1155	94.958975
Synthetic	--	validation	49	135	8.669080
Synthetic	--	test	89	321	27.178842
Synthetic	--	Total	497	1611	130.806897
Both	--	train	359	1523	127.696398
Both	--	validation	49	184	11.193588
Both	--	test	89	482	40.877733
Both	--	Total	497	2189	179.767718

Metadata

The dataset metadata is provided in three files:

• distinct_pieces.csv is a list of distinct pieces in this dataset, together with the allocated train/vadlidation/test split.
• metadata_R.csv provides the metadata for all the performances in the Real recording subset.
• metadata_S.csv provides the metadata for all the perofrmances in the Synthetic subset.

The parameters in the two metadata_X.csv file are:

• performance_id: The ID of the performance in this dataset. Performances from the Real recording subset have IDs starting with R_ and those from the Synthetic subset have IDs starting with S_.
• composer: composer of the music piece.
• piece_id: ID of the corresponding music piece, this is in line with the piece ID provided in distinct_pieces.csv.
• title: title of the music pieces, in line with the title in distinct_pieces.csv.
• source: the source dataset of the performance, can be "MAPS", "ASAP" or "CPM".
• performance_audio_external: path to the performance audio in the source dataset.
• performance_MIDI_external: path to the performance MIDI in the source dataset.
• MIDI_score_external: path to the MIDI score in the source dataset.
• performance_beat_annotation_external: path to the performance beat annotation in the source dataset.
• score_beat_annotation_external: path to the score beat annotation in the source dataset.
• folder: folder to the audio, MIDI and annotation files.
• performance_audio: performance audio file.
• performance_MIDI: performance MIDI file.
• MIDI_score: MIDI score file.
• aligned: True if the performance and score are aligned.
• performance_beat_annotation: performance beat annotation file.
• score_beat_annotation: # score beat annotation file.
• duration: duration of the performance in seconds.
• split: train/validation/test split.

The corresponding files are provided in the following locations:

• audio_files/{folder}/{performance_audio}
• {folder}/{performance_MIDI}
• {folder}/{MIDI_score}
• {folder}/{performance_beat_annotation}
• {folder}/{score_beat_annotation}

Reminders

• This dataset is created for Audio-to-Score Transcription, however, the voice information in the MIDI socres is not checked and it's suggested not to use it as ground truth annotation.
• there are 83 performances in total whose hand part is not equal to 2 (range from 1 to 10 parts).
• 30 performances are not aligned with the corresponding score. This is because of some errors made during the performance.