Update README.md

README.md

@@ -14,25 +14,18 @@ size_categories:
   - 1K<n<10K
 ---
 
-# 
+# MERP — Music Emotion Recognition with Profile Information
 
-
-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.
+Music is capable of conveying many emotions. The level and type of emotion perceived by a listener, however, is highly subjective. In this study, we present the **Music Emotion Recognition with Profile information dataset (MERP)**. This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The songs were selected from the Free Music Archive using an innovative method — a Triple Neural Network with the OpenSmile toolkit — to identify 50 songs with the most distinctive emotions, chosen to fully cover the four quadrants of the valence–arousal space. Four additional songs were selected from the DEAM dataset as anchor tracks for quality filtering. A total of 452 participants annotated the dataset, with 277 remaining after thorough cleaning. Their demographic information, listening preferences, and musical background were recorded.
 
 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) |
@@ -49,7 +42,7 @@ Song IDs follow the pattern `{folder}_{track}` (e.g., `00_35`, `deam_115`). Audi
 ## 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. |
@@ -80,7 +73,7 @@ The original processing pipeline then applied **per-rater rescaling** (each rate
 ### `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. |
@@ -90,7 +83,7 @@ The original processing pipeline then applied **per-rater rescaling** (each rate
 ### `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. |
@@ -109,7 +102,6 @@ The original processing pipeline then applied **per-rater rescaling** (each rate
 
 ```python
 import pandas as pd
-from datasets import load_dataset
 
 # Load filtered annotations
 ann = pd.read_parquet("annotations_filtered.parquet")
@@ -139,7 +131,7 @@ merged = ann.merge(profiles, on="rater_id", how="left")
 ```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},
+  author  = {Koh, En Yan and Cheuk, Kin Wai and Heung, Kwan Yee and Agres, Kat R. and Herremans, Dorien},
   journal = {Sensors},
   volume  = {23},
   number  = {1},