用多通道麦克风去除噪声段，提升心音信号识病准确率
Noise-Robust Contrastive Learning with an MFCC-Conformer For Coronary Artery Disease Detection
- 双麦克风识别并剔除含强非平稳噪声的音频段
- 在297人数据集上达78.4%准确率，比无去噪提升4.1%
- 适合真实场景下心音疾病检测的低噪声模型开发
心血管疾病是全球主要死因，冠状动脉疾病（CAD）占其最大比例。近年利用心音图（PCG）信号检测CAD受到关注，但在低噪声和理想传感器位置下表现良好。多通道技术更抗噪，但真实数据中仍面临挑战。本文提出一种新型多通道能量基噪声段剔除算法，使用心音与噪声参考麦克风，在训练前丢弃含大量非平稳噪声的音频段。基于Conformer的分类器采用多通道梅尔频率倒谱系数（MFCCs），进一步增强抗噪能力。该方法在297名受试者上达到78.4%准确率和78.2%平衡准确率，相比无噪声段剔除提升4.1%和4.3%。
原文摘要 · Abstract (English)
Cardiovascular diseases (CVD) are the leading cause of death worldwide, with coronary artery disease (CAD) comprising the largest subcategory of CVDs. Recently, there has been increased focus on detecting CAD using phonocardiogram (PCG) signals, with high success in clinical environments with low noise and optimal sensor placement. Multichannel techniques have been found to be more robust to noise; however, achieving robust performance on real-world data remains a challenge. This work utilises a novel multichannel energy-based noisy-segment rejection algorithm, using heart and noise-reference microphones, to discard audio segments with large amounts of nonstationary noise before training a deep learning classifier. This conformer-based classifier takes mel-frequency cepstral coefficients (MFCCs) from multiple channels, further helping improve the model's noise robustness. The proposed method achieved 78.4% accuracy and 78.2% balanced accuracy on 297 subjects, representing improvements of 4.1% and 4.3%, respectively, compared to training without noisy-segment rejection.
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