---
license: cc-by-4.0
task_categories:
  - audio-classification
tags:
  - drums
  - percussion
  - music-information-retrieval
  - drum-transcription
  - midi
  - electronic-drums
  - augmented
  - room-impulse-response
  - domain-adaptation
  - pre-computed-features
pretty_name: "Expanded Groove MIDI Dataset — Augmented (E-GMD-Aug)"
size_categories:
  - 100K<n<1M
---

# Expanded Groove MIDI Dataset — Augmented (E-GMD-Aug)

## Quick Start

```python
import pyarrow.parquet as pq
from huggingface_hub import hf_hub_download

# Download a single shard
path = hf_hub_download(
    "schismaudio/e-gmd-aug",
    filename="features/train-00000.parquet",
    repo_type="dataset",
)
table = pq.read_table(path)
print(f"Rows: {table.num_rows}, Columns: {table.column_names}")
```

## Dataset Description

**E-GMD-Aug** is an augmented derivative of the [Expanded Groove MIDI Dataset (E-GMD)](https://huggingface.co/datasets/schismaudio/e-gmd), designed for training automatic drum transcription (ADT) models that generalize from synthesized to real-world acoustic drum recordings.

The original E-GMD contains ~49,000 VST-rendered drum recordings that sound clean and synthetic. Models trained on this data suffer a domain gap when applied to real-world recordings with room acoustics, microphone coloration, and background noise. E-GMD-Aug addresses this by applying three waveform-level augmentations to each training track **before** computing mel spectrograms:

1. **Room Impulse Response (RIR) convolution** — Convolves dry audio with real and simulated RIRs from [OpenSLR-28](https://huggingface.co/datasets/schismaudio/openslr-rirs) (60,000+ RIRs), simulating diverse room acoustics.
2. **Parametric EQ** — Applies random low shelf, mid peak, and high shelf filters, simulating microphone and mixing coloration.
3. **Background noise mixing** — Mixes in point-source noise recordings at 20–40 dB SNR, simulating ambient room noise.

Each training track produces **1 dry (unaugmented) copy + 3 augmented copies** (4x data multiplier). Augmentation parameters are randomized per copy and stored in the `augmentation` column for reproducibility.

This dataset contains **pre-computed features** (mel spectrograms + onset/velocity targets), not raw audio. It is designed for direct use with [DrumscribbleCNN](https://github.com/zakkeown/drumscribble).

## Dataset Structure

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `mel_spectrogram` | `binary` | 128-band mel spectrogram, float32 (128 × n_frames) |
| `onset_targets` | `binary` | Onset target matrix, float32 (26 × n_frames) |
| `velocity_targets` | `binary` | Velocity target matrix, float32 (26 × n_frames) |
| `n_frames` | `int64` | Number of time frames |
| `n_mels` | `int64` | Number of mel bands (128) |
| `n_classes` | `int64` | Number of instrument classes (26) |
| `sample_rate` | `int64` | Audio sample rate used (16000) |
| `hop_length` | `int64` | STFT hop length (256) |
| `fps` | `float64` | Frames per second (62.5) |
| `duration` | `float64` | Duration in seconds |
| `split` | `string` | Always "train" (val/test are unaugmented) |
| `augmentation` | `string` | Empty for dry copy; JSON with augmentation params for augmented copies |
| `source_audio` | `string` | Original audio filename |
| `style` | `string` | Musical style (e.g. rock, funk, jazz) |
| `bpm` | `float64` | Tempo in BPM |
| `drummer` | `string` | Drummer ID |
| `session` | `string` | Recording session |
| `beat_type` | `string` | "beat" or "fill" |
| `time_signature` | `string` | Time signature (e.g. 4-4) |
| `kit_name` | `string` | VST drum kit name |
| `source_id` | `string` | Source performance ID |

### Data Splits

| Split | Entries | Rows (1 dry + 3 aug) | Shards |
|-------|---------|----------------------|--------|
| `train` | 35,217 | 140,868 | 410 |

Only the train split is augmented. Validation and test splits are served unaugmented from the original [schismaudio/e-gmd](https://huggingface.co/datasets/schismaudio/e-gmd) repo.

### File Layout

```
features/
  train-00000.parquet
  train-00001.parquet
  ...
  train-00409.parquet
```

### Augmentation Parameters

Each augmented row stores its parameters in the `augmentation` column as JSON:

```json
{
  "rir_idx": 42531,
  "wet_mix": 0.65,
  "low_shelf_db": -3.21,
  "high_shelf_db": 2.45,
  "low_shelf_freq": 125.0,
  "high_shelf_freq": 6200.0,
  "mid_freq": 1250.0,
  "mid_db": 1.85,
  "mid_q": 1.2,
  "noise_idx": 312,
  "snr_db": 28.5
}
```

Dry (unaugmented) copies have an empty string in the `augmentation` column.

## Augmentation Details

### RIR Convolution

RIRs are sourced from [schismaudio/openslr-rirs](https://huggingface.co/datasets/schismaudio/openslr-rirs) (OpenSLR-28):
- ~60,000 simulated RIRs across diverse room geometries
- 417 real isotropic RIRs recorded in actual rooms
- Wet/dry mix ratio randomized between 0.3–0.9

### Parametric EQ

Three-band parametric equalizer:
- **Low shelf**: 80–200 Hz, ±6 dB
- **Mid peak**: 300–3000 Hz, ±4 dB, Q 0.7–2.0
- **High shelf**: 4000–8000 Hz, ±6 dB

### Background Noise

Point-source noise recordings from OpenSLR-28 (843 noise WAVs):
- SNR randomized between 20–40 dB
- Noise is looped if shorter than the audio

## Usage with DrumscribbleCNN

```python
from drumscribble.data.features import ParquetFeaturesDataset
from huggingface_hub import HfApi, hf_hub_download

# Download all train shards
api = HfApi()
files = [f for f in api.list_repo_files("schismaudio/e-gmd-aug", repo_type="dataset")
         if f.startswith("features/train-") and f.endswith(".parquet")]
paths = [hf_hub_download("schismaudio/e-gmd-aug", f, repo_type="dataset") for f in files]

# Create dataset (lazy loading — one shard in memory at a time)
dataset = ParquetFeaturesDataset(paths, chunk_frames=625)
print(f"Training chunks: {len(dataset):,}")
```

## Dataset Creation

Generated by [`compute_features_aug.py`](https://github.com/zakkeown/drumscribble/blob/feat/hf-dataset-ecosystem/scripts/hf_upload/compute_features_aug.py), a PEP 723 UV script run as an HF Job on L4 GPU hardware. The pipeline:

1. Downloads E-GMD raw audio from Google Cloud Storage (~90 GB)
2. Downloads OpenSLR RIRs from HF Hub
3. For each training track: computes 1 dry + 3 augmented mel spectrograms
4. Streams sharded Parquet files to HF Hub (delete-after-upload to manage disk)

## Related Datasets

- [schismaudio/e-gmd](https://huggingface.co/datasets/schismaudio/e-gmd) — Original E-GMD with raw audio + pre-computed features (unaugmented)
- [schismaudio/star-drums-aug](https://huggingface.co/datasets/schismaudio/star-drums-aug) — Augmented STAR dataset (same pipeline)
- [schismaudio/openslr-rirs](https://huggingface.co/datasets/schismaudio/openslr-rirs) — Room impulse responses used for augmentation

## Citation

```bibtex
@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

This dataset is released under the [Creative Commons Attribution 4.0 International License (CC-BY 4.0)](https://creativecommons.org/licenses/by/4.0/), the same license as the original E-GMD.