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MADB-Dataset

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MADB: Music Aesthetics Dataset and Benchmark

Dataset Description

MADB is a large-scale dataset for music aesthetic evaluation, designed to support research on multi-dimensional and subjective music perception.

The dataset contains approximately 10,000 music tracks, each annotated by multiple trained annotators across 10 perceptual dimensions and one overall score. In addition, each track includes textual comments and semantic tags (genre and mood), enabling multimodal learning.

Data Composition

Audio

All audio files are stored under data/audio/ (mp3 format)
1730 tracks are generated by Suno and Levo
4400 tracks are from the Muchin dataset: https://github.com/CarlWangChina/MuChin
Remaining tracks are collected from diverse online sources

Annotations

Annotations are stored under data/annotation/:

avg_score.csv: average score for each dimension per track
MADB_data.csv: full annotation data, including:
- per-annotator scores
- textual comments
- genre and mood tags
split_val_seed5.csv: the validate set with random seed 5

Sample

Samples are stored under sample/:

sample_audio: 200 audios selected from validate set:
- 100 audios randomly selected from Muchin
- 50 audios from levo
- 50 audios from suno
sample_embedding: embedding extracted from sample audios:
- clap: extracted with original clap
- muq: extracted with original muq
- clap_com: extracted with clap after adaption with comments
- clap_com_tag: extracted with clap after adaption with comments and tags
sample_ids.csv: contains all sample audios' id

Annotation Framework

Each track is rated across the following dimensions:

Melody perception
Melody emotion
Arrangement perception
Arrangement emotion
Rhythm perception
Structure perception
Performance and singing mood
Enunciation and singing skill
Performance skill
Sound effect perception
Overall score

Some dimensions may be not applicable, in which case a value of 0 is assigned.

Annotation Process

Annotators have at least 3 years of formal music training
All annotators hold at least a bachelor's degree
Quality control is conducted by experts with 10+ years of professional experience
Each track is rated by multiple annotators

Intended Use

This dataset is designed for:

Music aesthetic evaluation
Multimodal learning (audio + text + tags)
Music understanding and analysis
Evaluation of generative music systems

Licensing and Usage

Audio data may be subject to original copyright restrictions
Users should ensure compliance with the original data sources
This dataset is intended for research purposes only

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