English(EN) SonicCaps: Large-Scale Diverse and Fine-Grained Captioning for Improved Audio-Retrieval

新的SonicCaps数据集通过多样化字幕增强音频语言模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员推出了SonicCaps，这是一个新推出的、大规模的数据集，旨在改进音频语言建模和音频检索。该数据集包含约1500万个字幕，与70万个音频片段配对，使用Qwen3-Omni多模态模型生成。SonicCaps旨在通过提供多样化、细粒度的字幕来克服现有数据集的局限性，这些字幕能够捕捉声学细节并反映听觉感知的模糊性。人类评估表明，SonicCaps字幕被认为更具描述性和精确性，从而在训练CLAP模型进行音频检索和分类任务时提高了性能。 AI

影响为训练音频语言模型提供了更丰富的数据集，有望提高AI对音频内容的理解和检索能力。

排序理由该集群描述了在arXiv上发布的新数据集和研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Signal score: 22 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了在arXiv上发布的新数据集和研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Zineb Lahrichi, Marc Ferras, Ga\"el Richard, Geoffroy Peeters · 2026-09-03 04:00

SonicCaps：大规模多样化细粒度字幕，用于改进音频检索

arXiv:2609.02343v1 Announce Type: cross Abstract: Recent advances in audio-language modeling have been driven by large-scale audio captioning datasets. However, existing datasets remain limited by low semantic diversity, generic descriptions lacking acoustic details, and one-to-o…

本文如何被排名

报道来源 [1]

SonicCaps：大规模多样化细粒度字幕，用于改进音频检索

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