---
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."
}
```