Datasets:
File size: 4,220 Bytes
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dataset_info:
features:
- name: identifier
dtype: string
- name: audio
dtype: audio
- name: question
dtype: string
- name: answer
dtype: string
- name: distractor_1
dtype: string
- name: distractor_2
dtype: string
- name: distractor_3
dtype: string
- name: main_category
dtype: string
- name: secondary_categories
dtype: string
- name: difficulty
dtype: string
- name: excerpt_start_time
dtype: string
- name: excerpt_end_time
dtype: string
- name: song_link
dtype: string
- name: jamendo_id
dtype: string
- name: song_name
dtype: string
- name: artist_name
dtype: string
- name: album_name
dtype: string
- name: song_license_url
dtype: string
splits:
- name: test
num_bytes: 476422419
num_examples: 320
download_size: 476401140
dataset_size: 476422419
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: cc-by-4.0
task_categories:
- question-answering
language:
- en
tags:
- music
size_categories:
- n<1K
---
# HumMusQA: A Human-written Music Understanding QA Benchmark Dataset
**Authors:** Benno Weck, Pablo Puentes, Andrea Poltronieri, Satyajeet Prabhu, Dmitry Bogdanov
HumMusQA is a multiple-choice question answering dataset designed to test music understanding in Large Audio-Language Models (LALMs).
## Dataset Highlights
- ✍️ 320 hand-written multiple-choice questions curated and validated by experts with musical training
- 🎵 108 Creative Commons-licensed music tracks sourced from Jamendo
- ⏱️ Music recordings ranging from 30 to 90 seconds
## Loading the data
```python
from datasets import load_dataset
dataset = load_dataset("mtg-upf/HumMusQA", split="test")
```
## Licensing
- The **dataset annotations** are licensed under **Creative Commons Attribution 4.0 (CC BY 4.0)**.
- Each **audio track** follows its **own Creative Commons license**, as specified in the dataset metadata.
Users are responsible for complying with the license terms of each individual audio track.
## Paper
- Paper DOI: https://doi.org/10.18653/v1/2026.nlp4musa-1.9
- Zenodo DOI: https://doi.org/10.5281/zenodo.18462523
- arXiv DOI: https://doi.org/10.48550/arXiv.2603.27877
## Citation
If you use this dataset, please cite [our paper](https://arxiv.org/abs/2603.27877):
> Benno Weck, Pablo Puentes, Andrea Poltronieri, Satyajeet Prabhu, and Dmitry Bogdanov. 2026. HumMusQA: A Human-written Music Understanding QA Benchmark Dataset. In Proceedings of the 4th Workshop on NLP for Music and Audio (NLP4MusA 2026), pages 58–67, Rabat, Morocco. Association for Computational Linguistics.
### BibTeX
```bibtex
@inproceedings{weck-etal-2026-hummusqa,
title = "{H}um{M}us{QA}: A Human-written Music Understanding {QA} Benchmark Dataset",
author = "Weck, Benno and
Puentes, Pablo and
Poltronieri, Andrea and
Prabhu, Satyajeet and
Bogdanov, Dmitry",
editor = "Epure, Elena V. and
Oramas, Sergio and
Doh, SeungHeon and
Ramoneda, Pedro and
Kruspe, Anna and
Sordo, Mohamed",
booktitle = "Proceedings of the 4th Workshop on {NLP} for Music and Audio ({NLP}4{M}us{A} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4musa-1.9/",
doi = "10.18653/v1/2026.nlp4musa-1.9",
pages = "58--67",
ISBN = "979-8-89176-369-2",
abstract = "The evaluation of music understanding in Large Audio-Language Models (LALMs) requires a rigorously defined benchmark that truly tests whether models can perceive and interpret music, a standard that current data methodologies frequently fail to meet.This paper introduces a meticulously structured approach to music evaluation, proposing a new dataset of 320 hand-written questions curated and validated by experts with musical training, arguing that such focused, manual curation is superior for probing complex audio comprehension.To demonstrate the use of the dataset, we benchmark six state-of-the-art LALMs and additionally test their robustness to uni-modal shortcuts."
}
``` |