# Audiocards: Structured Metadata Improves Audio Language Models For Sound Design

arXiv: https://arxiv.org/abs/2602.13835 · HTML: https://mlsanity.com/paper/2602.13835/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [An Evaluation Framework for Structured Audio Captions Validated by Controlled Perturbations](https://mlsanity.com/paper/2607.21424/) (2026)
- [SonicCaps: Large-Scale Diverse and Fine-Grained Captioning for Improved Audio-Retrieval](https://mlsanity.com/paper/2609.02343/) (2026)

## Abstract (arXiv)

Sound designers search for sounds in large sound effects libraries using aspects such as sound class or visual context. However, the metadata needed for such search is often missing or incomplete, and requires significant manual effort to add. Existing solutions to automate this task by generating metadata, i.e. captioning, and search using learned embeddings, i.e. text-audio retrieval, are not trained on metadata with the structure and information pertinent to sound design. To this end we propose audiocards, structured metadata grounded in acoustic attributes and sonic descriptors, by exploiting the world knowledge of LLMs. We show that training on audiocards improves downstream text-audio retrieval, descriptive captioning, and metadata generation on professional sound effects libraries. Moreover, audiocards also improve performance on general audio captioning and retrieval over the baseline single-sentence captioning approach. We release a curated dataset of sound effects audiocards to invite further research in audio language modeling for sound design.