Data-Balanced Curriculum Learning for Audio Question Answering
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916834581676032 |
|---|---|
| author | Wijngaard, Gijs Formisano, Elia Esposito, Michele Dumontier, Michel |
| author_facet | Wijngaard, Gijs Formisano, Elia Esposito, Michele Dumontier, Michel |
| contents | Audio question answering (AQA) requires models to understand acoustic content and perform complex reasoning. Current models struggle with dataset imbalances and unstable training dynamics. This work combines curriculum learning with statistical data balancing to address these challenges. The method labels question difficulty using language models, then trains progressively from easy to hard examples. Statistical filtering removes overrepresented audio categories, and guided decoding constrains outputs to valid multiple-choice formats. Experiments on the DCASE 2025 training set and five additional public datasets show that data curation improves accuracy by 11.7% over baseline models, achieving 64.2% on the DCASE 2025 benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06815 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Balanced Curriculum Learning for Audio Question Answering Wijngaard, Gijs Formisano, Elia Esposito, Michele Dumontier, Michel Sound Audio and Speech Processing Audio question answering (AQA) requires models to understand acoustic content and perform complex reasoning. Current models struggle with dataset imbalances and unstable training dynamics. This work combines curriculum learning with statistical data balancing to address these challenges. The method labels question difficulty using language models, then trains progressively from easy to hard examples. Statistical filtering removes overrepresented audio categories, and guided decoding constrains outputs to valid multiple-choice formats. Experiments on the DCASE 2025 training set and five additional public datasets show that data curation improves accuracy by 11.7% over baseline models, achieving 64.2% on the DCASE 2025 benchmark. |
| title | Data-Balanced Curriculum Learning for Audio Question Answering |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.06815 |