用咳嗽声区分五类呼吸道疾病，手机就能用。
CoughSense: Five-Class Respiratory Disease Classification via Whisper Encoder Fine-Tuning and Dual-Encoder Cross-Attention Fusion with Balanced Contrastive Learning
- 聚焦前200个关键音帧，解决短咳嗽被长音频稀释的问题。
- 在四大数据集上达82.3%平衡准确率，五类召回均超74%。
- 适合做移动端呼吸疾病初筛，尤其对资源有限地区有价值。
自动化咳嗽分析为低成本呼吸系统筛查提供了可能，但现有研究多仅限于二分类的新冠检测。实际应用需从一次手机录音中区分多种呼吸道疾病。本文提出CoughSense系统，可将咳嗽录音分为五类：健康、新冠、哮喘或其它呼吸疾病、支气管炎、肺炎。我们整合了四个公开数据集（Coswara、CoughVID、Virufy、华西儿科咳嗽数据集）共18,301条录音，采用OpenAI Whisper编码器作为预训练主干网络。核心创新是主动帧QKV注意力池化，仅保留前200个（共1500个）编码器标记，避免3秒咳嗽仅占150个标记导致的沉默稀释问题。训练中通过加权随机采样、SpecAugment、强制少数类配对的混合增强、监督对比辅助损失、FiLM症状条件控制及梯度反向域适应来应对19:1的类别不平衡和跨数据集域偏移。双编码器模型通过交叉注意力融合Whisper与OPERA-CT呼吸基础模型。在五折交叉验证中，CoughSense（Whisper-tiny，8.6M参数）达到82.3%平衡准确率（宏F1=0.817，AUC=0.941），较ImageNet预训练EfficientNet-B2高11.1点，较从零训练的ViT高29.6点。所有五类召回率均超过74%，四类超过80%。双编码器模型达85.4%平衡准确率。主动帧池化在消融实验中贡献最大，提升5.1个百分点，适用于任何使用Whisper作为主干的短音频任务。
原文摘要 · Abstract (English)
Automated cough analysis offers a path to low-cost respiratory screening, but most existing work stops at binary COVID-19 detection. A practical tool needs to tell apart several respiratory conditions from one cough recording on a consumer smartphone. We present CoughSense, a system that sorts cough recordings into five classes. These are healthy, COVID-19, asthma or respiratory condition, bronchitis, and pneumonia. We aggregated 18,301 recordings from four public datasets (Coswara, CoughVID, Virufy, and the West China Hospital Pediatric Cough Dataset) and used the OpenAI Whisper encoder as a pretrained backbone for cough disease classification. The main contribution is active-frame QKV attention pooling, which restricts attention to the first 200 of 1500 encoder tokens. This avoids the silence-dilution problem that arises because a 3-second cough fills only 150 tokens of Whisper's 30-second input window. Other training parts handle the 19 to 1 class imbalance and the four-dataset domain shift. These include WeightedRandomSampler, SpecAugment, Balanced Mixup with forced minority pairing, a supervised contrastive auxiliary loss, FiLM symptom conditioning, and gradient-reversal domain adaptation. A dual-encoder model fuses Whisper with the OPERA-CT respiratory foundation model through cross-attention. CoughSense (Whisper-tiny, 8.6M parameters) reached 82.3 percent balanced accuracy on five-fold cross-validation (macro-F1 of 0.817, AUC of 0.941). It beat an ImageNet-pretrained EfficientNet-B2 by 11.1 points and a ViT trained from scratch by 29.6 points. All five classes passed 74 percent recall and four of five passed 80 percent. The dual-encoder model reached 85.4 percent balanced accuracy. Active-frame pooling is the largest single contributor across all ablation components at 5.1 points, which should help any short-audio task using Whisper as a backbone.
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