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AudioCapBench: Quick Evaluation on Audio Captioning across Sound, Music, and Speech
arXiv – CS AI|Jielin Qiu, Jianguo Zhang, Zixiang Chen, Liangwei Yang, Ming Zhu, Juntao Tan, Haolin Chen, Wenting Zhao, Rithesh Murthy, Roshan Ram, Akshara Prabhakar, Shelby Heinecke, Caiming, Xiong, Silvio Savarese, Huan Wang||1 views
🤖AI Summary
Researchers introduce AudioCapBench, a new benchmark for evaluating how well large multimodal AI models can generate captions for audio content across sound, music, and speech domains. The study tested 13 models from OpenAI and Google Gemini, finding that Gemini models generally outperformed OpenAI in overall captioning quality, though all models struggled most with music captioning.
Key Takeaways
- →AudioCapBench evaluates AI models on audio captioning across three domains: environmental sound, music, and speech with 1,000 curated samples.
- →Gemini 3 Pro achieved the highest overall score of 6.00/10, while OpenAI models showed lower hallucination rates.
- →All tested models performed best on speech captioning and worst on music captioning tasks.
- →The benchmark uses both traditional metrics and an LLM-as-Judge framework to assess accuracy, completeness, and hallucination.
- →The benchmark and evaluation code are being released as open-source tools for reproducible audio AI research.
Read Original →via arXiv – CS AI
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