Datasets:

RS2002
/

RPMC-L2

task_categories:
  - other
license: cc-by-4.0
arxiv: 2506.01482
language:
  - en
tags:
  - audio
  - music
  - stage-lighting
  - audio-visual
pretty_name: RPMC-L2

RPMC-L2

Paper | Code | Model | Zenodo

The Rock, Punk, Metal, and Core - Livehouse Lighting (RPMC-L2) dataset contains synchronized music and lighting data collected from professional live performance venues. This is the first stage lighting dataset designed to treat Automatic Stage Lighting Control (ASLC) as a generative task, introduced in the paper "Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?".

Dataset Description

Repository: RS2002/Skip-BART
Paper: Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?
Contact: zzhaock@connect.ust.hk
Collector: Zijian Zhao
Dataset Processor: Dian Jin
Organization: Tokamak Disruption Band
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 Generative 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)

Citation

@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}
}