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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2505.07365 |
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| _version_ | 1866910045472555008 |
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| author | Yang, Chao-Han Huck Ghosh, Sreyan Wang, Qing Kim, Jaeyeon Hong, Hengyi Kumar, Sonal Zhong, Guirui Kong, Zhifeng Sakshi, S Lokegaonkar, Vaibhavi Nieto, Oriol Duraiswami, Ramani Manocha, Dinesh Kim, Gunhee Du, Jun Valle, Rafael Catanzaro, Bryan |
| author_facet | Yang, Chao-Han Huck Ghosh, Sreyan Wang, Qing Kim, Jaeyeon Hong, Hengyi Kumar, Sonal Zhong, Guirui Kong, Zhifeng Sakshi, S Lokegaonkar, Vaibhavi Nieto, Oriol Duraiswami, Ramani Manocha, Dinesh Kim, Gunhee Du, Jun Valle, Rafael Catanzaro, Bryan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07365 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning Yang, Chao-Han Huck Ghosh, Sreyan Wang, Qing Kim, Jaeyeon Hong, Hengyi Kumar, Sonal Zhong, Guirui Kong, Zhifeng Sakshi, S Lokegaonkar, Vaibhavi Nieto, Oriol Duraiswami, Ramani Manocha, Dinesh Kim, Gunhee Du, Jun Valle, Rafael Catanzaro, Bryan Sound Artificial Intelligence Computation and Language Multimedia Audio and Speech Processing 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. |
| title | Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning |
| topic | Sound Artificial Intelligence Computation and Language Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.07365 |