Add normalized Parquet: README.md

README.md

@@ -1,11 +1,240 @@
 ---
 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
 ---
 
-This is a multi-label music emotion recognition dataset that contains more than 16k songs. 
+# S16k — Multi-label Music Emotion Recognition from NetEase Cloud Music
 
-The dataset was obtained from NetEase Cloud Music, with specific details referenced from the paper https://doi.org/10.1007/s00530-025-01701-z
+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.
 
-The NPZ file contains the MFCC features extracted by librosa from the middle 30 seconds of all songs. The playlist IDs in the JSON file correspond to the song IDs in the CSV file.
+Reference paper: https://doi.org/10.1007/s00530-025-01701-z
 
-songs_9822 is a balanced multi-label subset of the original S16k. It contains 9822 entries with each emotion label appears ~4850 counts. 
+## 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
+
+```python
+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:
+
+```python
+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:
+
+```python
+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**:
+
+```python
+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.
+
+```bibtex
+@article{s16k,
+  doi = {10.1007/s00530-025-01701-z},
+  journal = {Multimedia Systems},
+  title = {A multi-label music emotion recognition dataset from NetEase Cloud Music}
+}
+```