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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.14307 |
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| _version_ | 1866915625600811008 |
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| author | Gibier, Marcel Celton, Nolwenn Duroselle, Raphaël Serrano, Pierre Boeffard, Olivier Bonastre, Jean-François |
| author_facet | Gibier, Marcel Celton, Nolwenn Duroselle, Raphaël Serrano, Pierre Boeffard, Olivier Bonastre, Jean-François |
| contents | In this report, we describe our submission to Track 5 of the DCASE 2025 Challenge for the task of Audio Question Answering(AQA). Our system leverages the SSL backbone BEATs to extract frame-level audio features, which are then processed by a classification head to generate segment-level predictions of acoustic events, following the Audioset ontology. These segment-level predictions are subsequently calibrated before producing event-level predictions. Finally, these predictions are incorporated into a structured prompt, along with the question and candidate answers. This prompt is then fed to a fine-tuned version of Qwen2.5-7B-Instruct, trained using the GRPO algorithm with a simple reward function. Our method achieves an accuracy of 62.6 % on the development set, demonstrating the effectiveness of combining acoustic event reasoning with instruction-tuned large language models for AQA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14307 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Audio Question Answering with GRPO-Based Fine-Tuning and Calibrated Segment-Level Predictions Gibier, Marcel Celton, Nolwenn Duroselle, Raphaël Serrano, Pierre Boeffard, Olivier Bonastre, Jean-François Sound Machine Learning In this report, we describe our submission to Track 5 of the DCASE 2025 Challenge for the task of Audio Question Answering(AQA). Our system leverages the SSL backbone BEATs to extract frame-level audio features, which are then processed by a classification head to generate segment-level predictions of acoustic events, following the Audioset ontology. These segment-level predictions are subsequently calibrated before producing event-level predictions. Finally, these predictions are incorporated into a structured prompt, along with the question and candidate answers. This prompt is then fed to a fine-tuned version of Qwen2.5-7B-Instruct, trained using the GRPO algorithm with a simple reward function. Our method achieves an accuracy of 62.6 % on the development set, demonstrating the effectiveness of combining acoustic event reasoning with instruction-tuned large language models for AQA. |
| title | Audio Question Answering with GRPO-Based Fine-Tuning and Calibrated Segment-Level Predictions |
| topic | Sound Machine Learning |
| url | https://arxiv.org/abs/2511.14307 |