NaturNestAI/electronic-music-knowledge

Electronic Music Knowledge

The largest open electronic music metadata dataset. 18.3M tracks, 1.4M artists, 353K labels, 832 genres with evolution graph.

Built for DJ Treta — an autonomous AI DJ — but useful for any music AI research.

Dataset Summary

Config	Rows	Description
tracks	18,315,675	Electronic music tracks with title, artist, genre/style, label, year, country
artists	1,424,582	Artists with primary genres, labels, country, active years, track count
labels	352,984	Record labels with genres, country, founding year
genres	832	Electronic genre taxonomy from Ishkur's Guide + Discogs (166 with BPM ranges)
genre_graph	352	Genre evolution relationships with time ranges

Quick Start

from datasets import load_dataset

# Load tracks (default config)
tracks = load_dataset("NaturNestAI/electronic-music-knowledge", "tracks", split="train")

# Load other configs
artists = load_dataset("NaturNestAI/electronic-music-knowledge", "artists", split="train")
genres = load_dataset("NaturNestAI/electronic-music-knowledge", "genres", split="train")
labels = load_dataset("NaturNestAI/electronic-music-knowledge", "labels", split="train")
graph = load_dataset("NaturNestAI/electronic-music-knowledge", "genre_graph", split="train")

Examples

Find melodic techno tracks

tracks = load_dataset("NaturNestAI/electronic-music-knowledge", "tracks", split="train")
melodic = tracks.filter(lambda x: x["subgenre"] == "Melodic House & Techno")
print(f"{len(melodic)} melodic techno tracks")

Find artists on a label

artists = load_dataset("NaturNestAI/electronic-music-knowledge", "artists", split="train")
drumcode = artists.filter(lambda x: x["labels"] and "Drumcode" in str(x["labels"]))

Genre evolution graph

graph = load_dataset("NaturNestAI/electronic-music-knowledge", "genre_graph", split="train")
# What influenced a genre?
influences = graph.filter(lambda x: x["target_genre"] == "melodictechno")

Schema

tracks

Column	Type	Description
id	string	Unique track identifier
title	string	Track/release title
artist_name	string	Artist name
discogs_artist_id	string	Discogs artist ID
discogs_release_id	string	Discogs release ID
subgenre	string	Primary Discogs style (e.g., "Melodic House & Techno")
styles_json	string	JSON array of all Discogs styles
label	string	Record label name
country	string	Release country
year	int	Release year
search_query	string	Pre-computed "Artist - Title" for YouTube/music search
source	string	Data source ("discogs" or "ishkur")

artists

Column	Type	Description
id	string	"discogs:{artist_id}"
name	string	Artist name
primary_genres	string	Primary genre/style
labels	string	Known label
country	string	Country of origin
active_since	int	Year of earliest release
track_count	int	Number of tracks in dataset

genres (Ishkur subset has BPM ranges)

Column	Type	Description
id	string	Genre slug
name	string	Genre name
scene	string	Ishkur scene grouping (House, Techno, Trance, etc.)
bpm_low / bpm_high	int	Typical BPM range
energy_typical	int	Typical energy level (1-10)
aliases	string	Alternative genre names

labels

Column	Type	Description
id	string	"discogs:{label_id}"
name	string	Label name
primary_genres	string	Primary genre
country	string	Country
founded_year	int	Year of earliest release

genre_graph

Column	Type	Description
source_genre	string	Genre that influenced
target_genre	string	Genre that was influenced
start_year / end_year	int	Time range of influence

Data Sources

Source	License	Contribution
Discogs Data Dump (April 2026)	CC0 1.0	4.9M electronic releases, 1.4M artists, 353K labels, 666 styles
Ishkur's Guide to Electronic Music v3	Open	166 genre taxonomy with BPM ranges, 11K tracks, evolution graph

Planned Enrichment (v2)

• BPM and musical key from AcousticBrainz (29.5M tracks, CC0)
• Artist similarity graph
• MusicBrainz cross-reference IDs
• DJ set transition data

Pipeline

Built with VeltriaAI/music-intelligence — extensible source adapter architecture. Add new data sources by dropping a Python file.

Citation

@dataset{electronic_music_knowledge_2026,
  title={Electronic Music Knowledge},
  author={NaturNest AI},
  year={2026},
  url={https://huggingface.co/datasets/NaturNestAI/electronic-music-knowledge},
  license={CC0-1.0}
}

License

CC0 1.0 Universal — No Rights Reserved.