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.
What is AVD?
AVD is a three-dimensional model of how people perceive music:
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:
Take the Spotify features that tap complexity and timbral sophistication: acousticness, instrumentalness, danceability, speechiness, loudness, and tempo.
Residualise them against Arousal (energy) and Valence (valence) so Depth is orthogonal to the other two axes.
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.
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.
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
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.
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.
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}
}
