README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: dev
        path: dev.jsonl
license: apache-2.0

DCASE 2026 Task 5 Audio-Dependent Question Answering (ADQA) Development Set

This is the official Development Set for DCASE 2026 Challenge Task 5: Audio-Dependent Question Answering (ADQA).

The ADQA task focuses on addressing "Textual Hallucination" in Large Audio-Language Models (LALMs) — where models pass audio understanding benchmarks by relying on text prompts and internal linguistic priors rather than actual audio perception. ADQA introduces a rigorous evaluation framework using Audio-Dependency Filtering (ADF) to ensure questions cannot be answered through common sense or text-only reasoning.

Audio-Dependency Filtering (ADF)

All samples in this development set undergo a rigorous four-step ADF hard-filtering process to guarantee genuine audio dependence:

1. Silent Audio Filtering: Questions solvable by LALMs without audio are removed.
2. LLM Common-sense Check: Ensures no external knowledge alone can solve the question.
3. Perplexity-based Soft Filtering: Eliminates samples with text-based statistical shortcuts.
4. Manual Verification: Final human-in-the-loop check for ground-truth accuracy.

Statistics

Metric	Count
Total Samples	1,607
Unique Audio Files	1,607

Data Sources

The development set is composed of two parts:

• Existing Benchmarks: A portion of the samples is derived from established audio understanding benchmarks, including MMAU, MMAR, and MMSU. These samples cover a wide range of audio understanding tasks such as speech, music, and sound perception.
• Human-Annotated Questions: The remaining majority consists of newly constructed, human-annotated multiple-choice questions based on diverse audio sources, designed to further challenge models on real-world audio comprehension.

All samples undergo the four-step Audio-Dependency Filtering (ADF) process described above.

Directory Structure

DCASE2026-Task5-DevSet/
├── dev.jsonl                # Main data file (1,607 samples, shuffled)
├── dev_audios/              # Audio files (1,607 .wav files)
└── README.md

Data Format

Each entry in dev.jsonl is a JSON object with the following fields:

Field	Type	Description
id	string	Unique sample identifier (e.g., dev_0001)
audio_path	string	Relative path to audio file
question_text	string	Question text
answer	string	Correct answer
multi_choice	list[string]	Answer choices

Example

{
  "id": "dev_0001",
  "audio_path": "dev_audios/dev_0001.wav",
  "question_text": "What is the speaker's primary emotion in this audio?",
  "answer": "Happiness",
  "multi_choice": ["Sadness", "Happiness", "Anger", "Fear"]
}

Submission Format

The system output file should be a .csv file with the following two columns:

Column	Description
question	The question ID (e.g., dev_0001)
answer	The system's answer, must match one of the given choices

License

This dataset is distributed under the Apache-2.0 license.

Citation

If you use this development set or participate in DCASE 2026 Task 5, please cite:

@article{he2025measuring,
  title={Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio Language Models},
  author={He, Haolin and Du, Xingjian and Sun, Renhe and Dai, Zheqi and Xiao, Yujia and Yang, Mingru and Zhou, Jiayi and Li, Xiquan and Liu, Zhengxi and Liang, Zining and others},
  journal={arXiv preprint arXiv:2509.21060},
  year={2025}
}

@article{he2026summary,
  title={Summary of DCASE 2026 Task 5: Audio-Dependent Question Answering},
  author={He, Haolin and Sun, Renhe and Dai, Zheqi and Du, Xingjian and Wu, Chunyat and Liang, Zining and Liu, Zhengxi and Lei, Jiahe and Wang, Runbang and Zhou, Jiayi and Yang, Mingru and Li, Xiquan and Chen, Yun and Chen, Xie and Duan, Zhiyao and Wang, Weiqiang and Plumbley, Mark D. and Liu, Jian and Kong, Qiuqiang},
  journal={arXiv preprint arXiv:2607.18718},
  year={2026}
}