# ACES: Evaluating Automated Audio Captioning Models on the Semantics of Sounds

arXiv: https://arxiv.org/abs/2403.18572 · HTML: https://mlsanity.com/paper/2403.18572/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [BRACE: A Benchmark for Robust Audio Caption Quality Evaluation](https://mlsanity.com/paper/2512.10403/) (2025)
- [CLAIR-A: Leveraging Large Language Models to Judge Audio Captions](https://mlsanity.com/paper/2409.12962/) (2024)
- [AURA Score: A Metric For Holistic Audio Question Answering Evaluation](https://mlsanity.com/paper/2510.04934/) (2025)

## Abstract (arXiv)

Automated Audio Captioning is a multimodal task that aims to convert audio content into natural language. The assessment of audio captioning systems is typically based on quantitative metrics applied to text data. Previous studies have employed metrics derived from machine translation and image captioning to evaluate the quality of generated audio captions. Drawing inspiration from auditory cognitive neuroscience research, we introduce a novel metric approach -- Audio Captioning Evaluation on Semantics of Sound (ACES). ACES takes into account how human listeners parse semantic information from sounds, providing a novel and comprehensive evaluation perspective for automated audio captioning systems. ACES combines semantic similarities and semantic entity labeling. ACES outperforms similar automated audio captioning metrics on the Clotho-Eval FENSE benchmark in two evaluation categories.