An Evaluation Framework for Structured Audio Captions Validated by Controlled Perturbations
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Computer Science > Computation and Language
arXiv:2607.21424 (cs)
[Submitted on 23 Jul 2026]
Title:An Evaluation Framework for Structured Audio Captions Validated by Controlled Perturbations
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Abstract:Recent advancements in automated audio captioning (AAC) have shifted from monolithic sentence generation toward structured formats that explicitly disentangle distinct acoustic and semantic properties. However, evaluating this heterogeneous data remains a significant challenge. Existing caption metrics focus on flat textual outputs and fail to reliably assess multimodal attributes. To bridge this gap, we propose a multi-axis evaluation framework tailored for structured audio descriptions. Building on the AudioCards dataset, we evaluate outputs across five orthogonal axes: tag-sets, descriptions, logical reasoning, numeric measurements, and spectral profiles. Our approach combines Large Language Model (LLM) judges to capture semantic nuance with deterministic computational metrics to precisely measure acoustic deviations. To rigorously validate the reliability of this framework, we introduce a controlled perturbation testing protocol that injects typed, graded errors into groundtruth annotations. Our results demonstrate that this framework successfully distinguishes meaning-preserving paraphrases from genuine semantic and acoustic corruptions.
| Comments: | submitted to DCASE 2026 |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2607.21424 [cs.CL] |
| (or arXiv:2607.21424v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.21424
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled An Evaluation Framework for Structured Audio Captions Validated by Controlled Perturbations, by Liang-Yuan Wu and 3 other authors
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