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arXiv · 2505.07365

Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning

Chao-Han Huck Yang·Sreyan Ghosh·Qing Wang·Jaeyeon Kim·Hengyi Hong·Sonal Kumar·Guirui Zhong·Zhifeng Kong·S Sakshi·Vaibhavi Lokegaonkar·Oriol Nieto·Ramani Duraiswami·Dinesh Manocha·Gunhee Kim·Jun Du·Rafael Valle·Bryan Catanzaro
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Abstract

We present Task 5 of the DCASE 2025 Challenge: an Audio Question Answering (AQA) benchmark spanning multiple domains of sound understanding. This task defines three QA subsets (Bioacoustics, Temporal Soundscapes, and Complex QA) to test audio-language models on interactive question-answering over diverse acoustic scenes. We describe the dataset composition (from marine mammal calls to soundscapes and complex real-world clips), the evaluation protocol (top-1 accuracy with answer-shuffling robustness), and baseline systems (Qwen2-Audio-7B, AudioFlamingo 2, Gemini-2-Flash). Preliminary results on the development set are compared, showing strong variation across models and subsets. This challenge aims to advance the audio understanding and reasoning capabilities of audio-language models toward human-level acuity, which are crucial for enabling AI agents to perceive and interact about the world effectively.

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Chao-Han Huck Yang, Sreyan Ghosh, Qing Wang, Jaeyeon Kim, Hengyi Hong, Sonal Kumar, Guirui Zhong, Zhifeng Kong, S Sakshi, Vaibhavi Lokegaonkar, Oriol Nieto, Ramani Duraiswami, Dinesh Manocha, Gunhee Kim, Jun Du, Rafael Valle, Bryan Catanzaro. 2026-03-09. Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning. https://arxiv.org/abs/2505.07365

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