license: cc-by-nc-sa-4.0
task_categories:
- audio-classification
- text-classification
language:
- zh
- en
- ja
- ko
tags:
- music
- music-emotion-recognition
- multi-label
- netease
pretty_name: S16k
size_categories:
- 100K<n<1M
configs:
- config_name: songs
data_files: songs.parquet
- config_name: songs_9822_balanced
data_files: songs_9822.parquet
- config_name: playlists
data_files: playlists.parquet
- config_name: song_playlist
data_files: song_playlist.parquet
- config_name: tag_taxonomy
data_files: tag_taxonomy.parquet
S16k — Multi-label Music Emotion Recognition from NetEase Cloud Music
169,148 songs with multi-label emotion annotations, the 46,613 user-curated playlists the annotations are derived from, pre-extracted audio features, and a class-balanced subset.
Reference paper: https://doi.org/10.1007/s00530-025-01701-z
How the labels are defined
Every playlist on NetEase Cloud Music (网易云音乐) carries editorial tags such as 流行,
治愈, 夜晚. A song inherits the tags of every playlist that contains it, and the value stored
is a count: emo_healing = 3 means three playlists holding this song are tagged 治愈.
These are platform and community tags, not controlled psychological ratings. Treat them accordingly when comparing against annotation-based corpora such as DEAM or PMEmo.
Files
| File | Rows | Size | Content |
|---|---|---|---|
songs.parquet |
169,148 | 52 MB | Main table: metadata, 74 tag columns, emotion labels |
songs_9822.parquet |
9,822 | 5 MB | Class-balanced subset, same schema plus unique_id |
playlists.parquet |
46,613 | 10 MB | Source playlists with tags and popularity statistics |
song_playlist.parquet |
477,216 | 2.4 MB | Song ↔ playlist relation, long format |
tag_taxonomy.parquet / .csv |
74 | <1 MB | Tag column ↔ English ↔ Chinese ↔ group mapping |
songs_169148_vggish.npz |
169,097 | 2.7 GB | VGGish embeddings |
songs_9822_vggish.npz |
9,822 | 0.16 GB | VGGish embeddings for the balanced subset |
spectrograms_169148.npz |
169,148 | 17.3 GB | Log-mel spectrograms in dB |
Audio features are computed from the middle 30 seconds of each track. Raw audio is not redistributed.
Quick start
import pandas as pd
songs = pd.read_parquet("songs.parquet")
tax = pd.read_parquet("tag_taxonomy.parquet")
emo_cols = tax.loc[tax.group == "emo", "column"].tolist()
X = songs[emo_cols] # tag counts
Y = (songs[emo_cols] > 0).astype(int) # binary multi-label targets
songs.parquet
Identifiers and metadata
| Key | Type | Description |
|---|---|---|
song_id |
int64 | Primary key, unique across all rows |
alt_ids |
list<int64> | Further NetEase IDs pointing at the same track. Non-empty on 8,418 rows, up to 4 entries |
n_ids |
int8 | 1 + len(alt_ids) |
name |
string | Track title, Chinese verbatim. 4 nulls |
artist |
string | 5 nulls |
language |
dictionary<string> | 42 values. instrumental 66,244 · en 42,747 · zh 39,692 · ja 13,113 · ko 2,504 |
duration_ms |
int32, nullable | Null on 21,798 rows (12.9%) where the source carries no duration. Those rows are otherwise complete and should not be dropped |
lyric |
string | Present on 102,903 rows; absent almost exactly where language == "instrumental" |
n_playlists |
int16 | Playlists containing this song. Mean 2.82, max 198 |
Tag columns
74 columns holding counts in the range 0–133, prefixed by group following the official NetEase tag taxonomy:
| Prefix | Group | Count | Examples |
|---|---|---|---|
lang_ |
Language 语种 | 6 | lang_mandarin 华语 · lang_western 欧美 · lang_cantonese 粤语 |
genre_ |
Genre 风格 | 25 | genre_pop 流行 · genre_rock 摇滚 · genre_new_age New Age |
scene_ |
Scene 场景 | 12 | scene_night 夜晚 · scene_driving 驾车 · scene_study 学习 |
emo_ |
Emotion 情感 | 12 | emo_healing 治愈 · emo_sadness 伤感 · emo_missing 思念 |
theme_ |
Theme 主题 | 18 | theme_acg ACG · theme_soundtrack 影视原声 · theme_ktv KTV |
misc_ |
Outside the official taxonomy | 1 | misc_sexy 性感 |
tag_taxonomy.parquet holds the complete mapping with columns column, en, zh,
group, is_emotion_label, emotion_index.
Four tags carried by the data but not listed on the official taxonomy page are grouped by
meaning: 小语种 under lang, 另类/独立 and 音乐剧 under genre, 性感 under misc.
性感 sits outside emo so that the emotion label set stays at exactly 12 classes.
Emotion label vectors
Three fixed-length list columns sharing one order:
| Key | Type | Description |
|---|---|---|
emo_tag_counts |
list<int16>[12] | Playlist tag counts |
emo_ratio |
list<float32>[12] | emo_tag_counts normalised to sum 1 |
emo_label |
list<int8>[12] | 1 where the count is above 0 |
The order lives in the Parquet schema metadata:
import pyarrow.parquet as pq, json
t = pq.read_table("songs.parquet")
EMO_ORDER = json.loads(t.schema.metadata[b"emotion_order"])
# ['Healing','Nostalgia','Excitement','Sadness','Romantic','Quiet',
# 'Happiness','Loneliness','Touching','Missing','Fresh','Relaxation']
The three list columns and the 12 scalar emo_* columns carry the same information; use
whichever suits the task.
Every song carries at least one emotion label, 2.50 on average. Positive rates run from
emo_healing 35.7% and emo_relaxation 32.8% down to emo_missing 11.3%.
songs_9822.parquet
A subset chosen so the 12 emotions have near-equal positive support:
| Healing | Nostalgia | Excitement | Sadness | Romantic | Quiet | Happiness | Loneliness | Touching | Missing | Fresh | Relaxation | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| positives | 4,949 | 4,873 | 4,873 | 4,874 | 4,874 | 4,873 | 4,873 | 4,874 | 4,874 | 4,875 | 4,873 | 4,879 |
Mean 5.96 labels per song, above the 2.50 of the full table, since balancing favours
multi-labelled tracks. The schema matches songs.parquet with one addition:
| Key | Type | Description |
|---|---|---|
unique_id |
int64 | Row identifier within the subset |
Paired features: songs_9822_vggish.npz.
playlists.parquet
| Key | Type | Description |
|---|---|---|
playlist_id |
int64 | Join key for song_playlist.parquet |
name |
string | Playlist title, Chinese verbatim |
tags |
list<string> | Chinese tag strings, e.g. ["感动","治愈","思念"]. 75 distinct values, 1–6 per playlist, most commonly 3 |
n_tags |
int64 | |
confidence |
dictionary<string> | normal 23,567 · vip 15,865 · official 7,181 |
play_count |
int64 | |
subscribed_count |
int64 | |
share_count |
int64 | |
comment_count |
int64 | |
create_time |
timestamp | |
description |
string | Curator's free text, Chinese verbatim |
creator_nickname |
string | |
creator_user_type |
int64 | |
creator_gender |
int64 | 0 unspecified · 1 male · 2 female |
tags holds Chinese strings while songs.parquet uses English column names; the zh and
column fields of tag_taxonomy.parquet connect the two.
The 75 Chinese tag strings map onto 74 columns because the source spells New Age two ways,
one with a regular space (U+0020) and one with a non-breaking space (U+00A0). Both denote the
same tag and share the column genre_new_age.
song_playlist.parquet
| Key | Type |
|---|---|
song_id |
int64 |
playlist_id |
int64 |
477,216 rows. Every playlist_id resolves in playlists.parquet. 43,558 of the 46,613
playlists are referenced by at least one song.
This table makes the labels re-derivable under a different aggregation. The stored counts weight every playlist equally; the relation lets you weight by popularity or restrict to curated sources:
rel = pd.read_parquet("song_playlist.parquet")
plf = pd.read_parquet("playlists.parquet")
official = plf[plf.confidence == "official"]
sub = rel[rel.playlist_id.isin(official.playlist_id)]
weight = plf.set_index("playlist_id")["play_count"]
Aggregating the Chinese tags over each song's playlists reproduces the stored tag counts
exactly.
Audio features
Both NPZ archives are keyed by song_id as a string:
import numpy as np
z = np.load("songs_169148_vggish.npz")
emb = z["1000155"] # (31, 128) float32
| File | Array per song | Description |
|---|---|---|
songs_169148_vggish.npz |
(31, 128) float32, range 0–255 |
VGGish embeddings, one 128-d frame per ~0.96 s across the middle 30 s, post-processed quantised output. Covers 169,097 songs |
songs_9822_vggish.npz |
(31, 128) float32 |
The same for the balanced subset |
spectrograms_169148.npz |
(64, 469) float32, range −70.8–0 |
Log-mel spectrogram in dB, 64 mel bands × 469 frames across the middle 30 s. Covers 169,148 songs |
songs_169148_vggish.npz covers 169,097 of the 169,148 songs; check key membership before
indexing.
Licence and citation
CC BY-NC-SA 4.0. Only identifiers, metadata, tags and derived features are distributed; no audio.
@article{s16k,
doi = {10.1007/s00530-025-01701-z},
journal = {Multimedia Systems},
title = {A multi-label music emotion recognition dataset from NetEase Cloud Music}
}