X - ACE : Explainable and Multi-factor Audio Captioning Evaluation (2024)

Abstract: Automated audio captioning (AAC) aims to generate descriptions based on audio input, attracting exploration of emerging audio language models (ALMs). However, current evaluation metrics only provide a single score to assess the overall quality of captions without characterizing the nuanced difference by systematically going through an evaluation checklist. To this end, we propose the explainable and multi-factor audio captioning evaluation (X-ACE) paradigm. X-ACE identifies four main factors that constitute the majority of audio features, specifically sound event, source, attribute and relation. To assess a given caption from an ALM, it is firstly transformed into an audio graph, where each node denotes an entity in the caption and corresponds to a factor. On the one hand, graph matching is conducted from part to whole for a holistic assessment. On the other hand, the nodes contained within each factor are aggregated to measure the factor-level performance. The pros and cons of an ALM can be explicitly and clearly demonstrated through X-ACE, pointing out the direction for further improvements. Experiments show that X-ACE exhibits better correlation with human perception and can detect mismatches sensitively.

## introduction recognizing the pivotal role of auditory perception in human cognition, there is a trend among multimodal large language models (mllms) @xcite to broaden their scope into the audio-language domain. it has given rise to audio language models (alms) @xcite @xcite , with a notable emphasis on automated audio captioning a man speaks nearby as a few hissing sounds occur three times. ## x-ace in this section, we commence with defining the audio factors in section 3.1, which are key components of x-ace for assessment in section 3.2. ## conclusion the current assessment of alms only provide an overall score, presenting challenges for model refinement. in response, this paper introduces explainable and multi-factor evaluation paradigm x-ace for aac, defining sound event, source, attribute, and relation as four factors tailored for the audio description. furthermore, we provided a dataset audiocaps-f to enhance evaluation granularity. x-ace exhibits remarkable alignment with human perception and shows a nuanced capacity to distinguish model errors. our analysis, derived from outcomes of x-ace, illuminates substantial variances among mainstream models in audio attribute, and temporal sequence description. while differences are less pronounced in sound events, considerable room for improvement exists across factors.