Create README.md

README.md

@@ -0,0 +1,86 @@
+# RPMC-L2
+
+The description is generated by Grok3.
+
+## Dataset Description
+
+- **Repository:** [RS2002/Skip-BART: Official Repository for The Paper, Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?](https://github.com/RS2002/Skip-BART)
+- **Paper:** [Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?](https://arxiv.org/abs/2506.01482)
+- **Contact:** zzhaock@connect.ust.hk
+- **Collector:** Zijian Zhao
+- **Dataset Processer:** Dian Jin
+- **Organization:** [Tokamak Disruption Band](https://tokamak-disruption.netlify.app/)
+- **Dataset Summary:**
+  The Rock, Punk, Metal, and Core - Livehouse Lighting (RPMC-L2) dataset contains synchronized music and lighting data collected from professional live performance venues in the genres of Rock, Punk, Metal, and Core. The dataset includes 699 files in HDF5 format, totaling approximately 40 GB, designed to study the relationship between music features and lighting effects in live performances.
+- **Tasks:** Music-to-Lighting Relationship Analysis, Audio-Visual Synchronization, Cross-Domain Tasks.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance is an HDF5 file (`.h5`) containing synchronized music and lighting data for a specific live performance. The dataset is split into multiple parts (`RPMC_L2_part_aa`, `RPMC_L2_part_ab`, etc.) that can be merged into a single `RPMC_L2.h5` file. Each file is organized into two main groups: `music` and `light`, with the following features:
+
+- **Music Features**: Audio-related features stored as `np.ndarray` arrays with shape `(X, L)`, where `L` is the sequence length.
+- **Light Features**: Lighting-related data stored as `np.ndarray` arrays, primarily threshold data with shape `(F, 3, 256)`.
+
+### Data Fields
+
+| Group | Feature            | Shape       | Description                                                  |
+| ----- | ------------------ | ----------- | ------------------------------------------------------------ |
+| music | openl3             | (512, L)    | OpenL3 deep audio embedding                                  |
+| music | mel_spectrogram    | (128, L)    | Mel spectrogram                                              |
+| music | mel_spectrogram_db | (128, L)    | Mel spectrogram in decibels                                  |
+| music | cqt                | (84, L)     | Constant-Q transform (CQT)                                   |
+| music | stft               | (1025, L)   | Short-time Fourier transform (STFT)                          |
+| music | mfcc               | (128, L)    | Mel-frequency cepstral coefficients                          |
+| music | chroma_stft        | (12, L)     | Chroma features from STFT                                    |
+| music | chroma_cqt         | (12, L)     | Chroma features from CQT                                     |
+| music | chroma_cens        | (12, L)     | Chroma Energy Normalized Statistics                          |
+| music | spectral_centroids | (1, L)      | Spectral centroid                                            |
+| music | spectral_bandwidth | (1, L)      | Spectral bandwidth                                           |
+| music | spectral_contrast  | (7, L)      | Spectral contrast                                            |
+| music | spectral_rolloff   | (1, L)      | Spectral rolloff frequency                                   |
+| music | zero_crossing_rate | (1, L)      | Zero-crossing rate                                           |
+| light | threshold          | (F, 3, 256) | Frame-specific light threshold data (Hue: 0–179, Saturation: 0–255, Value: 0–255) |
+
+### Data Splits
+
+The dataset consists of 699 files, organized by file hashes (top-level keys in the HDF5 file). There are no predefined splits; users can process the merged `RPMC_L2.h5` file to create custom train/validation/test splits based on their research needs.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to facilitate research on the relationship between music characteristics and lighting effects in live performance venues, enabling applications in automated lighting design, audio-visual synchronization, and immersive live experiences.
+
+### Source Data
+
+- Initial Data Collection:
+
+  Data was collected from professional live performance venues hosting Rock, Punk, Metal, and Core music genres. Music features were extracted from audio recordings, and lighting data was captured as threshold values (Hue, Saturation, Value) synchronized with the audio.  
+
+  - **Total Size:** ~40 GB  
+  - **Collection Method:** Professional live performance venues  
+  - **File Format:** HDF5 (`.h5`)
+
+- **Who are the source data producers?**
+  The data was collected by researchers or professionals in live music venues, capturing synchronized audio and lighting data.
+
+### Annotations
+
+- **Annotation Process:**
+  The dataset includes music features (e.g., mel spectrogram, MFCC) and lighting data (threshold values for Hue, Saturation, Value) automatically extracted and synchronized during data collection. No manual annotations were provided.  
+- **Who are the annotators?**
+  The dataset creators processed and organized the data using automated feature extraction tools.
+
+## Citation
+
+```bibtex
+@article{zhao2025automatic,
+  title={Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?},
+  author={Zhao, Zijian and Jin, Dian and Zhou, Zijing and Zhang, Xiaoyu},
+  journal={arXiv preprint arXiv:2506.01482},
+  year={2025}
+}
+```
+