Upload MERP dataset

README.md

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 ---
-license: cc-by-sa-4.0
+license: cc-by-4.0
+task_categories:
+  - audio-classification
+tags:
+  - music
+  - emotion
+  - valence
+  - arousal
+  - continuous-annotation
+  - music-emotion-recognition
+pretty_name: MERP — Music Emotion Recognition with Profile Information
+size_categories:
+  - 1K<n<10K
 ---
+
+# 
+
+
+MERP is a music dataset containing **continuous, dynamic valence and arousal ratings** collected from Amazon Mechanical Turk workers for 54 full-length music tracks. Alongside the annotations it provides **demographic and musical background profiles** for each rater.
+
+Source code and training scripts: [github.com/dorienh/MERP](https://github.com/dorienh/MERP)
+
+The dataset was introduced in:
+
+> Koh, E.Y.; Cheuk, K.W.; Heung, K.Y.; Agres, K.R.; Herremans, D.  
+> **MERP: A Music Dataset with Emotion Ratings and Raters' Profile Information.**  
+> *Sensors* 2023, 23, 382. https://doi.org/10.3390/s23010382
+
+---
+
+## Corpus
+
+| Property | Value |
+|---|---|
+| Tracks | 54 full-length songs |
+| Track source | 50 tracks from the [Free Music Archive](https://freemusicarchive.org/) (CC-licensed); 4 anchor tracks from [DEAM](https://cvml.unige.ch/databases/DEAM/) (also FMA/CC) |
+| Annotation resolution | 10 Hz (one rating per 0.1 s) |
+| Annotation scale | [−1, 1] for both valence and arousal |
+| MTurk batches | 4–6 (US, Master workers) and 7–8 (worldwide, non-Master) |
+| Total raters (raw) | 447 |
+| Total raters (filtered) | 277 |
+| Raters with profile data | 196 |
+
+Song IDs follow the pattern `{folder}_{track}` (e.g., `00_35`, `deam_115`). Audio files are in `audio/{song_id}.wav`.
+
+---
+
+## Files
+
+| File | Rows | Description |
+|---|---|---|
+| `annotations_filtered.parquet` | 3 482 | Quality-filtered annotations; per-rater rescaled to [−1, 1] and Savitzky-Golay smoothed (window 15, poly 2). This is the version used in the paper. |
+| `annotations_raw.parquet` | 6 176 | Raw MTurk slider output before any filtering or rescaling. |
+| `rater_profiles.parquet` | 196 | Demographic and musical background for raters who passed profile quality checks. |
+| `audio/*.wav` | 54 | Full-length audio tracks. |
+
+Rater IDs (`rater_001` … `rater_447`) are consistent across all three tables. Worker IDs from Amazon Mechanical Turk have been anonymized.
+
+---
+
+## Filtering steps (filtered split)
+
+The `annotations_filtered` split had 7 quality-control steps applied:
+
+1. Trials that were too short or too long relative to the audio duration were removed.
+2. Trials from raters with erroneous profile entries (e.g., training duration < 0 or > 100 years) were removed.
+3. Trials with long stagnant plateaus (≥ 300 identical consecutive values) were removed.
+4. Raters who did not complete all 4 DEAM anchor songs were removed.
+5. Raters whose valence or arousal range across all their trials was less than 1/3 of the full scale were removed.
+6. Annotation arrays were trimmed to match the audio feature length.
+7. Where a rater completed duplicate DEAM anchor sessions, the session with lower correlation to the DEAM reference annotations was discarded.
+
+The original processing pipeline then applied **per-rater rescaling** (each rater's min → −1, max → 1) and **Savitzky-Golay smoothing** (window=15, polynomial order=2) to produce the values stored in this split.
+
+---
+
+## Column descriptions
+
+### `annotations_filtered.parquet` / `annotations_raw.parquet`
+
+| Column | Type | Description |
+|---|---|---|
+| `rater_id` | str | Anonymized rater identifier (e.g., `rater_042`). Consistent with `rater_profiles.parquet`. |
+| `song_id` | str | Track identifier matching the `audio/` file stem (e.g., `00_35`, `deam_115`). |
+| `batch` | str | MTurk batch number (`"4"`–`"8"`). Batches 4–6 are US Master workers; 7–8 are worldwide non-Master. |
+| `arousal` | list[float] | Time-series arousal ratings at 10 Hz. |
+| `valence` | list[float] | Time-series valence ratings at 10 Hz. |
+
+### `rater_profiles.parquet`
+
+| Column | Type | Description |
+|---|---|---|
+| `rater_id` | str | Anonymized rater identifier. |
+| `master` | int | 1 = MTurk Master worker, 0 = non-Master. |
+| `age` | int | Rater's age in years. |
+| `country_enculturation` | str | Country where the rater grew up. |
+| `country_live` | str | Country where the rater currently lives. |
+| `fav_music_lang` | str | Preferred music language (e.g., `EN`, `ZH`). |
+| `gender` | str | Self-reported gender. |
+| `fav_genre` | str | Favourite music genre. |
+| `play_instrument` | str | Whether the rater plays an instrument (`Yes`/`No`). |
+| `training` | str | Whether the rater has formal music training (`Yes`/`No`). |
+| `training_duration` | int | Years of formal music training (0 if none). |
+
+---
+
+## Usage
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+# Load filtered annotations
+ann = pd.read_parquet("annotations_filtered.parquet")
+
+# Average valence per song across all raters
+avg = ann.groupby("song_id")["valence"].apply(
+    lambda arrs: [sum(x)/len(arrs) for x in zip(*arrs)]
+)
+
+# Join with rater profiles
+profiles = pd.read_parquet("rater_profiles.parquet")
+merged = ann.merge(profiles, on="rater_id", how="left")
+```
+
+---
+
+## License
+
+**Annotations and rater profiles:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+
+**Audio files:** All tracks are from the Free Music Archive under Creative Commons licenses (CC BY, CC BY-SA, or CC BY-NC depending on the track). Please check the FMA metadata for per-track license details.
+
+---
+
+## Citation
+
+```bibtex
+@article{koh2023merp,
+  title   = {{MERP}: A Music Dataset with Emotion Ratings and Raters' Profile Information},
+  author  = {Koh, Enyan and Cheuk, Kin Wai and Heung, Kwan Yee and Agres, Kat R. and Herremans, Dorien},
+  journal = {Sensors},
+  volume  = {23},
+  number  = {1},
+  pages   = {382},
+  year    = {2023},
+  doi     = {10.3390/s23010382}
+}
+```

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