flaser381/spotify-avd

Spotify AVD — Arousal · Valence · Depth

A large-scale, psychology-backed dataset of artist- and genre-level musical attribute profiles, computed from Spotify audio features. It pairs the well-known Arousal–Valence model with a derived Depth dimension, the three-attribute framework introduced by Greenberg et al. (2016) and later replicated with computer-extracted audio features by Fricke et al. (2018).

• 2,204,210 artists with popularity-weighted AVD means, per-axis spread, follower counts, and track coverage.
• 734 genres with the same aggregate statistics.
• Built from the audio-feature layer of the Spotify catalogue (~255M tracks) — this release publishes aggregate statistics only, not per-track audio data.

⚠️ Provenance & usage. This dataset contains transformative aggregate statistics derived from Spotify audio features and public artist metadata. The underlying audio features and metadata remain the property of Spotify AB. It is intended for non-commercial research. Not affiliated with or endorsed by Spotify.

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What is AVD?

AVD is a three-dimensional model of how people perceive music:

Axis	Everyday meaning	Audio basis here
A — Arousal	Energy, intensity, force vs. calmness	Spotify energy
V — Valence	Positive, happy, bright vs. negative, sad, dark	Spotify valence
D — Depth	Sophistication, complexity, thoughtfulness, poetic quality	Derived from Spotify features (see below)

The model comes from Greenberg et al. (2016), The Song Is You, who showed that listeners’ ratings of music collapse into these three components and that those components predict personality above and beyond demographics. Fricke et al. (2018) later demonstrated that the same AVD structure emerges from computer-extracted audio features, meaning the dimensions can be recovered automatically from the sound signal. That is what this dataset does at scale.

How Depth is derived

Spotify does not ship a “Depth” feature. We construct it as an acoustic-sophistication factor:

1. Take the Spotify features that tap complexity and timbral sophistication: acousticness, instrumentalness, danceability, speechiness, loudness, and tempo.
2. Residualise them against Arousal (energy) and Valence (valence) so Depth is orthogonal to the other two axes.
3. Extract the first principal component, sign-check it against genre intuition (classical, ambient, jazz = high; comedy, drill, spoken-word = low), and rescale to 0–1.

The result is a single, interpretable axis that is largely uncorrelated with Arousal and Valence.

Aggregation

Per-artist and per-genre values are (popularity+1)-weighted means over tracks, so an artist’s best-known sound dominates the profile. Each row also reports the standard deviation on each axis (var_a, var_v, var_d) and a composite spread score — the average standard deviation across AVD — which tells you how stylistically varied an artist or genre is.

Only tracks with a popularity of at least 5 enter the aggregates. This floor excludes the long tail of never-played catalogue entries (bootlegs, duplicates, mislabelled credits) that otherwise inflate track counts and spread without reflecting an artist’s actual sound.

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Files

artists_avd.parquet — 2,204,210 artists

column	type	meaning
artist_key	str	normalised (lowercased) artist name — join key
artist_name	str	display name
artist_id	str	Spotify artist id (base62)
a, v, d	float	Arousal, Valence, Depth on a 0–1 scale (popularity-weighted mean)
var_a, var_v, var_d	float	per-axis standard deviation across the artist’s tracks
spread	float	mean of the three std-devs — stylistic eclecticism/consistency
n_tracks	int	tracks with audio features used in the aggregate
followers	int	Spotify follower count

genres_avd.parquet — 734 genres

column	type	meaning
genre	str	Spotify genre label
n	int	tracks used for the aggregate
a, v, d	float	Arousal, Valence, Depth (0–1)
var_a, var_v, var_d	float	per-axis standard deviation
spread	float	mean of the three std-devs

Only genres with at least 10 tracks are included.

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A taste of the data

All values are on a 0–1 scale. The genre tables are restricted to genres with at least 1,000 tracks for stability; artist tables are restricted to artists with >1 million followers so the names are familiar.

Genres — highest Depth

genre	n tracks	a	v	d
ambient	93,174	0.236	0.131	0.787
classical piano	209,837	0.109	0.275	0.785
dark ambient	61,675	0.230	0.124	0.777
drone	51,043	0.294	0.130	0.776
space music	34,534	0.275	0.148	0.760

Genres — lowest Depth

genre	n tracks	a	v	d
comedy	71,028	0.597	0.509	0.156
spoken word	52,055	0.447	0.457	0.236
brooklyn drill	16,440	0.640	0.489	0.255
new york drill	23,099	0.629	0.479	0.259
uk drill	32,558	0.622	0.574	0.264

Genres — highest Valence

genre	n tracks	a	v	d
vallenato	36,785	0.689	0.839	0.552
duranguense	27,315	0.658	0.825	0.569
tropical music	58,531	0.652	0.823	0.563
merengue	49,977	0.732	0.812	0.564
cumbia	219,370	0.707	0.809	0.561

Genres — lowest Valence

genre	n tracks	a	v	d
dark ambient	61,675	0.230	0.124	0.777
drone	51,043	0.294	0.130	0.776
ambient	93,174	0.236	0.131	0.787
space music	34,534	0.275	0.148	0.760
black metal	51,649	0.815	0.165	0.698

Genres — highest Arousal

genre	n tracks	a	v	d
hardcore techno	39,996	0.934	0.268	0.530
deathcore	48,999	0.924	0.221	0.571
hardcore	103,663	0.922	0.289	0.518
frenchcore	61,114	0.918	0.261	0.457
gabber	39,131	0.916	0.278	0.486

Genres — lowest Arousal

genre	n tracks	a	v	d
classical piano	209,837	0.109	0.275	0.785
chamber music	172,209	0.131	0.289	0.718
requiem	104,235	0.139	0.224	0.679
classical	566,956	0.141	0.251	0.735
choral	140,617	0.174	0.210	0.652

Artists (>1M followers) — highest Depth

artist	followers	a	v	d
my bloody valentine	1,039,825	0.718	0.320	0.832
Frédéric Chopin	3,470,884	0.089	0.183	0.799
Max Richter	1,077,258	0.134	0.107	0.795
Ludovico Einaudi	3,777,203	0.143	0.109	0.786
Ludwig van Beethoven	5,565,695	0.117	0.257	0.785

Artists (>1M followers) — lowest Depth

artist	followers	a	v	d
Die drei ???	1,142,355	0.486	0.542	0.082
Shoreline Mafia	1,301,140	0.560	0.371	0.179
Ugly God	2,045,624	0.485	0.419	0.192
Blueface	6,819,253	0.564	0.491	0.194
King Von	6,433,332	0.610	0.412	0.195

Artists (>1M followers) — highest Valence

artist	followers	a	v	d
Jorge Oñate	1,051,332	0.683	0.945	0.579
Diomedes Diaz	5,667,222	0.672	0.935	0.593
El Coyote Y Su Banda Tierra Santa	1,688,623	0.579	0.928	0.616
Binomio de Oro	1,699,124	0.628	0.925	0.559
Grupo Laberinto	1,021,440	0.498	0.924	0.538

Artists (>1M followers) — lowest Valence

artist	followers	a	v	d
Claude Debussy	1,607,806	0.048	0.096	0.777
Sigur Rós	1,395,389	0.353	0.107	0.744
Max Richter	1,077,258	0.134	0.107	0.795
Ludovico Einaudi	3,777,203	0.143	0.109	0.786
Hans Zimmer	4,470,974	0.289	0.111	0.760

Artists (>1M followers) — highest Arousal

artist	followers	a	v	d
Cannibal Corpse	1,157,976	0.980	0.198	0.732
Slayer	4,260,332	0.966	0.183	0.578
Lamb of God	2,211,438	0.964	0.208	0.554
Amon Amarth	1,199,599	0.959	0.214	0.662
Killswitch Engage	2,063,594	0.953	0.297	0.566

Artists (>1M followers) — lowest Arousal

artist	followers	a	v	d
Claude Debussy	1,607,806	0.048	0.096	0.777
Franz Schubert	1,166,930	0.081	0.180	0.724
Frédéric Chopin	3,470,884	0.089	0.183	0.799
Johannes Brahms	1,040,044	0.102	0.223	0.774
Ludwig van Beethoven	5,565,695	0.117	0.257	0.785

Artists (>1M followers) — most eclectic vs. most consistent

spread is the average of var_a, var_v, and var_d. A high spread means the artist’s catalogue roams widely across AVD space; a low spread means the tracks cluster tightly around one signature. To keep “most consistent” meaningful, the consistency table requires at least 20 tracks.

Most eclectic

artist	followers	n tracks	a	v	d	spread
Toby Fox	1,730,867	466	0.509	0.584	0.757	0.262
Jim Morrison	1,896,305	52	0.422	0.432	0.455	0.244
Salman Khan	1,413,888	100	0.690	0.534	0.484	0.242
Böhse Onkelz	1,352,442	839	0.802	0.411	0.531	0.237
Santhosh Narayanan	3,087,709	965	0.569	0.476	0.476	0.236

Most consistent

artist	followers	n tracks	a	v	d	spread
Eslam Kabonga	1,956,555	658	0.886	0.794	0.401	0.065
Hamo Bika	2,353,706	404	0.883	0.815	0.427	0.070
Os Barões Da Pisadinha	6,826,354	507	0.875	0.913	0.669	0.073
JISOO	5,613,776	55	0.727	0.604	0.420	0.078
Claude Debussy	1,607,806	4,408	0.048	0.096	0.777	0.079

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Why this dataset is useful

Because AVD is grounded in music-psychology research, these aggregates are useful far beyond simple tagging:

• Recommendation & discovery — model similarity in a psychologically meaningful space instead of raw audio features.
• Genre taxonomy — compare genres on continuous emotion and complexity axes rather than folk labels.
• Playlist generation — build playlists that move through AVD space or stay within a narrow emotional pocket.
• Artist similarity — find artists with similar AVD signatures but different follower sizes, eras, or scenes.

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Quick start

from datasets import load_dataset

artists = load_dataset("<user>/spotify-avd", "artists", split="train")
genres  = load_dataset("<user>/spotify-avd", "genres",  split="train")

df = artists.to_pandas()

# Most eclectic well-known artists
df[df.followers > 1_000_000].nlargest(10, "spread")[
    ["artist_name", "a", "v", "d", "spread", "followers"]
]

# Chill, complex, low-arousal genres
genres_df = genres.to_pandas()
genres_df.query("a < 0.3 and d > 0.7").nsmallest(10, "v")[
    ["genre", "a", "v", "d", "n"]
]

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Limitations

• Arousal and Valence are Spotify’s estimates, not ground-truth human judgments. They are convenient and well-validated proxies, but still black-box predictions.
• Depth is a derived construct, not a directly measured label. We sign-checked it against genre intuition, but there is no public large-scale Depth ground-truth dataset.
• The snapshot reflects the 2025-07 Spotify catalogue; newer releases are absent.
• Aggregate means hide within-artist range — use spread and var_* when consistency matters.
• Spotify follower counts are a noisy proxy for prominence and vary over time.
• These are musical-attribute profiles only. They describe sound, not listeners, and carry no clinical or psychological-state meaning about any individual.

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Citation & references

If you use this dataset in research, please cite the underlying AVD work and this dataset:

@article{greenberg2016song,
  title={The Song Is You: Preferences for Musical Attribute Dimensions Reflect Personality},
  author={Greenberg, David M. and Kosinski, Michal and Stillwell, David J. and Monteiro, Brian L. and Levitin, Daniel J. and Rentfrow, Peter J.},
  journal={Social Psychological and Personality Science},
  volume={7},
  number={6},
  pages={597--605},
  year={2016}
}

@article{fricke2018computer,
  title={Computer-based music feature analysis mirrors human perception and can be used to measure individual music preference},
  author={Fricke, Kai R. and Greenberg, David M. and Rentfrow, Peter J. and Herzberg, Philipp Yorck},
  journal={Journal of Research in Personality},
  volume={75},
  pages={94--102},
  year={2018}
}

Dataset built with Spotilyze.