Datasets:

schismaudio
/

e-gmd

mel_spectrogram.npy listlengths 128 128	onset_targets.npy listlengths 26 26	params.json dict	velocity_targets.npy listlengths 26 26	__key__ stringlengths 40 60	__url__ stringclasses 3 values
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End of preview. Expand in Data Studio

Expanded Groove MIDI Dataset (E-GMD)

FLAC-compressed WebDataset mirror of the E-GMD dataset with precomputed drum transcription features.

Dataset Overview

Property	Value
Samples	45,537 (WAV + MIDI pairs)
Performances	1,059 unique across 43 drum kits
Drummers	10
Audio	16-bit FLAC, 44100 Hz, mono
Splits	train (35,217) / validation (5,031) / test (5,289)

Configs

Config	Content	Est. Size
`raw`	FLAC audio + MIDI per sample	~70 GB
`features`	Mel specs (128xT) + onset targets (26xT) + velocity targets (26xT)	~44 GB

Loading

Stream Raw Audio + MIDI

from datasets import load_dataset

ds = load_dataset("schismaudio/e-gmd", name="raw", split="train", streaming=True)
for example in ds:
    # audio.flac, midi.mid, metadata.json
    pass

Load Features

ds = load_dataset("schismaudio/e-gmd", name="features", split="train")
for example in ds:
    # mel_spectrogram.npy (128, T), onset_targets.npy (26, T), velocity_targets.npy (26, T), params.json
    pass

Feature Parameters

Parameter	Value
Sample rate	16000 Hz
Hop length	256
n_mels	128
n_classes	26 (GM drum pitches 35-60)
FPS	62.5

26-Class Drum Mapping

Class	MIDI	Instrument
0	35	Acoustic Bass Drum
1	36	Bass Drum 1
2	37	Side Stick
3	38	Acoustic Snare
4	39	Hand Clap
5	40	Electric Snare
6	41	Low Floor Tom
7	42	Closed Hi-Hat
8	43	High Floor Tom
9	44	Pedal Hi-Hat
10	45	Low Tom
11	46	Open Hi-Hat
12	47	Low-Mid Tom
13	48	Hi-Mid Tom
14	49	Crash Cymbal 1
15	50	High Tom
16	51	Ride Cymbal 1
17	52	Chinese Cymbal
18	53	Ride Bell
19	54	Tambourine
20	55	Splash Cymbal
21	56	Cowbell
22	57	Crash Cymbal 2
23	58	Vibraslap
24	59	Ride Cymbal 2
25	60	Hi Bongo

Pitch aliases: MIDI 22 → class 7 (Closed Hi-Hat), MIDI 26 → class 11 (Open Hi-Hat)

Citation

@article{callender2020improving,
  title={Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset},
  author={Callender, Lee and Hawthorne, Curtis and Engel, Jesse},
  journal={arXiv preprint arXiv:2004.00188},
  year={2020}
}

License

CC-BY-4.0

Downloads last month: 329

Size of the auto-converted Parquet files (First 5GB per split):

30.7 GB

Number of rows (First 5GB per split):

30,700

Estimated number of rows:

170,176

Paper for schismaudio/e-gmd

Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset

Paper • 2004.00188 • Published Apr 1, 2020