EDM-TECH Dataset is an open-source corpus of drum recordings produced in the techno genre, consisting of 11,270 WAV files paired with JSON metadata for supervised model training. It builds upon the earlier EDM-TR9 Dataset, offering a larger and more diverse set of training examples for generative AI music development.
The dataset was generated using custom scripting applied to a proprietary database of MIDI patterns and one-shot drum samples. Recordings primarily feature drum machine and percussive drum synths, complemented by additional samples of synthesizers and chords. Data augmentation included random sample-swapping, track isolation, pitch-shifting, equalization, and convolution reverb modeling. These strategies enhance model generalization by exposing training examples to diverse rhythms, sonic qualities, and ambient effects. Training examples were randomly mixed with varying signal levels and audio qualities, providing examples that reflect the evolving styles of techno music over the decades.
Its primary purpose is to provide accessible content for model development, audio research, and music applications. Example uses include text-to-audio, style transfer, feature extraction, tempo detection, audio classification, rhythm analysis, music information retrieval (MIR), sound design, and signal processing.
Specifications
- 11,270 audio loops (approximately 30 hours)
- 16-bit stereo WAV format, 44.1 kHz
- Tempo range: 128-150 BPM
- Paired label data (WAV + JSON)
- Variational drum mixes, rhythm patterns, and sounds
- Subgenre styles (modern, minimal, industrial, hardcore, 90s, drum machine)
A key map JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences.
See the examples folder to preview mp3 demos.
This dataset was compiled by WaivOps, a crowdsourced music project managed by Patchbanks. All recordings have been sourced from verified composers and providers for copyright clearance.
The EDM-TECH dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
Direct WAV Download (12.1 GB): edm_tech_drm_wav.tar.gz
Direct JSON Download (482.0 kB): edm_tech_drm_json.tar.gz
| Label | Reference |
|---|---|
| bpm | The tempo of the audio file |
| edm_tech_drm | Dataset name |
| id | Identification number |
| 000000 | Playlist track number |
If you use this dataset for a research or development project, please cite the following references:
@dataset{EDM-TECH Dataset,
author = {WaivOps},
title = {WaivOps EDM-TECH: Open Audio Resources for Machine Learning in Music},
year = {2025},
doi = {10.5281/zenodo.17584890},
url = {https://doi.org/10.5281/zenodo.17584890},
}Additional Info
For any questions or feedback please email info@patchbanks.com.