Multi-Domain Audio Question Answering Toward Acoustic Content Reasoning in The DCASE 2025 Challenge

• URL: http://arxiv.org/abs/2505.07365v1
• Date: Mon, 12 May 2025 09:04:16 GMT
• Title: Multi-Domain Audio Question Answering Toward Acoustic Content Reasoning in The DCASE 2025 Challenge
• Authors: 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,
• Abstract summary: This task defines three QA subsets to test audio-language models on interactive question-answering over diverse acoustic scenes.<n>Preliminary results on the development set are compared, showing strong variation across models and subsets.<n>This challenge aims to advance the audio understanding and reasoning capabilities of audio-language models toward human-level acuity.
• Score: 102.84031769492708
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation [55.607230723223346]
  This work presents a systematic study of Large Audio Model (LAM) as a Judge, AudioJudge, investigating whether it can provide a unified evaluation framework that addresses both challenges.<n>We explore AudioJudge across audio characteristic detection tasks, including pronunciation, speaking rate, speaker identification and speech quality, and system-level human preference simulation for automated benchmarking.<n>We introduce a multi-aspect ensemble AudioJudge to enable general-purpose multi-aspect audio evaluation. This method decomposes speech assessment into specialized judges for lexical content, speech quality, and paralinguistic features, achieving up to 0.91 Spearman correlation with human preferences on
  arXiv  Detail & Related papers  (2025-07-17T00:39:18Z)
• Audiopedia: Audio QA with Knowledge [0.0]
  We introduce Audiopedia, a novel task called Audio Question Answering with Knowledge.<n>Unlike traditional Audio Question Answering (AQA) benchmarks that focus on simple queries answerable from audio alone, Audiopedia targets knowledge-intensive questions.<n>We benchmark large audio language models (LALMs) on these sub-tasks and observe suboptimal performance.<n>We propose a generic framework that can be adapted to any LALM, equipping them with knowledge reasoning capabilities.
  arXiv  Detail & Related papers  (2024-12-29T23:48:35Z)
• Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoning [55.2480439325792]
  Large audio-language models (LALMs) have shown impressive capabilities in understanding and reasoning about audio and speech information.<n>These models still face challenges, including hallucinating non-existent sound events, misidentifying the order of sound events, and incorrectly attributing sound sources.
  arXiv  Detail & Related papers  (2024-10-21T15:55:27Z)
• Answering Diverse Questions via Text Attached with Key Audio-Visual Clues [24.347420432207283]
  We propose a framework for performing mutual correlation distillation (MCD) to aid question inference. We evaluate the proposed method on two publicly available datasets containing multiple question-and-answer pairs.
  arXiv  Detail & Related papers  (2024-03-11T12:51:37Z)
• AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension [95.8442896569132]
  We introduce AIR-Bench, the first benchmark to evaluate the ability of Large Audio-Language Models (LALMs) to understand various types of audio signals and interact with humans in the textual format. Results demonstrate a high level of consistency between GPT-4-based evaluation and human evaluation.
  arXiv  Detail & Related papers  (2024-02-12T15:41:22Z)
• Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models [98.34889301515412]
  We develop the Qwen-Audio model and address the limitation by scaling up audio-language pre-training to cover over 30 tasks and various audio types. Qwen-Audio achieves impressive performance across diverse benchmark tasks without requiring any task-specific fine-tuning. We further develop Qwen-Audio-Chat, which allows for input from various audios and text inputs, enabling multi-turn dialogues and supporting various audio-central scenarios.
  arXiv  Detail & Related papers  (2023-11-14T05:34:50Z)
• Learning to Answer Questions in Dynamic Audio-Visual Scenarios [81.19017026999218]
  We focus on the Audio-Visual Questioning (AVQA) task, which aims to answer questions regarding different visual objects sounds, and their associations in videos. Our dataset contains more than 45K question-answer pairs spanning over different modalities and question types. Our results demonstrate that AVQA benefits from multisensory perception and our model outperforms recent A-SIC, V-SIC, and AVQA approaches.
  arXiv  Detail & Related papers  (2022-03-26T13:03:42Z)
• Joint Speech Recognition and Audio Captioning [37.205642807313545]
  Speech samples recorded in both indoor and outdoor environments are often contaminated with secondary audio sources. We aim to bring together the growing field of automated audio captioning (AAC) and the thoroughly studied automatic speech recognition (ASR) We propose several approaches for end-to-end joint modeling of ASR and AAC tasks.
  arXiv  Detail & Related papers  (2022-02-03T04:42:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.