  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.
