English(EN) Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment

LLM增强的对齐改进了零样本呼吸音分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种新颖的零样本呼吸音分类框架，通过将自监督呼吸编码器与临床术语对齐。该方法利用医学LLM从元数据生成结构化报告，为对比学习创建语义锚点。该方法结合了基于sigmoid的对比损失、编码器的原生SSL目标以及相似性感知负采样，以改善病理边界区分。在多个任务和数据集上，该框架实现了61.3%的平均零样本AUC，优于CLAP和Qwen2-Audio等现有模型，并且仅使用显著更少的数据就达到了最高的线性探测AUC。 AI

影响这项研究可以通过对呼吸音进行更准确、更节省数据量的分析来增强医疗保健领域的诊断能力。

排序理由该集群包含一篇详细介绍AI驱动分类新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Mustafa Talha \.Ilerisoy, Hung Manh Pham, Mathias Funk, Mykola Pechenizkiy, Aaqib Saeed · 2026-09-02 04:00

通过LLM增强的音频-文本对齐实现零样本呼吸音分类

arXiv:2609.00055v1 Announce Type: cross Abstract: Self-supervised respiratory encoders lack semantic grounding in clinical domain needed for zero-shot inference, limiting their utility without task-specific labeled data. We propose a framework that aligns these encoders with medi…

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通过LLM增强的音频-文本对齐实现零样本呼吸音分类

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