TAU: A Benchmark for Cultural Sound Understanding Beyond Semantics

Yi-Cheng Lin, Yu-Hua Chen, 董家愷, Yueh-Hsuan Huang, 陳思齊, Yu-Chen Chen, Chih-Yao Chen, Yu-Jung Lin, Yu-Ling Chen, Zih-Yu Chen, I-Ning Tsai, Hsiu-Hsuan Wang, Ho-Lam Chung, Ke-Han Lu, Hung-yi Lee

TAU: A Benchmark for Cultural Sound Understanding Beyond Semantics: 3 upvotes on Hugging Face Daily Papers, #42 of 61 papers on 2025-10-01. Day-by-day upvote history.

Large audio-language models are advancing rapidly, yet most evaluations emphasize speech or globally sourced sounds, overlooking culturally distinctive cues. This gap raises a critical question: can current models generalize to localized, non-semantic audio that communities instantly recognize but outsiders do not? To address this, we present TAU (Taiwan Audio Understanding), a benchmark of everyday Taiwanese "soundmarks." TAU is built through a pipeline combining curated sources, human editing, and LLM-assisted question generation, producing 702 clips and 1,794 multiple-choice items that cannot be solved by transcripts alone. Experiments show that state-of-the-art LALMs, including Gemini 2.5 and Qwen2-Audio, perform far below local humans. TAU demonstrates the need for localized benchmarks to reveal cultural blind spots, guide more equitable multimodal evaluation, and ensure models serve communities beyond the global mainstream.

Paper page on Hugging Face · arXiv

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