English(EN) AudioFuse: Unified Spectral-Temporal Learning via a Hybrid ViT-1D CNN Architecture for Robust Phonocardiogram Classification

AudioFuse 结合 ViT 和 1D CNN 实现稳健的心音图分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了 AudioFuse，这是一种结合了 Vision Transformer (ViT) 和 1D 卷积神经网络 (CNN) 模型的心音图 (PCG) 分类新架构。这种混合方法同时从生物医学音频信号的光谱和时间数据表示中学习。在 PhysioNet 2016 数据集上，AudioFuse 取得了 0.8608 的具有竞争力的 ROC-AUC，优于其单独的基线模型。该架构在 PASCAL 数据集上还表现出对域迁移的卓越鲁棒性，在仅使用频谱图的基线模型显著退化的情况下，保持了高 ROC-AUC。 AI

影响引入了一种新颖的混合架构，用于改进生物医学音频信号分类，有望增强诊断工具。

排序理由详细介绍用于生物医学音频分类的新型混合 AI 架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Md. Saiful Bari Siddiqui, Utsab Saha · 2026-09-10 04:00

AudioFuse：通过混合 ViT-1D CNN 架构实现统一的谱时学习，用于稳健的心音图分类

arXiv:2509.23454v2 Announce Type: replace-cross Abstract: Biomedical audio signals, such as phonocardiograms (PCG), are inherently rhythmic and contain diagnostic information in both their spectral (tonal) and temporal domains. Standard 2D spectrograms provide rich spectral featu…

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AudioFuse：通过混合 ViT-1D CNN 架构实现统一的谱时学习，用于稳健的心音图分类

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