Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning
Chao-Han Huck Yang, Qing Wang, Jaeyeon Kim, Hengyi Hong, Sonal Kumar, Guirui Zhong, Zhifeng Kong, S Sakshi
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.
BibTeX
@inproceedings{icassp2026_multidomainaudio,
title = {Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning},
author = {Chao-Han Huck Yang and Qing Wang and Jaeyeon Kim and Hengyi Hong and Sonal Kumar and Guirui Zhong and Zhifeng Kong and S Sakshi and Oriol Nieto and Ramani Duraiswami and Gunhee Kim and Jun Du and Rafael Valle},
booktitle = {ICASSP 2026},
year = {2026}
}