对比三种声谱前端，提升心音异常检测准确率
Comparing Spectrogram Front-Ends for Abnormal Heart-Sound Detection with a Convolutional Neural Network
- 固定CNN模型，只替换音频转图像的声谱方法
- PCEN与多分辨率方法在PhysioNet上达91.6%准确率
- 模型关注低频区，符合心音生理特征
心脏病致死率高，早期通过听诊或录音检测可降低成本。本研究为二分类任务：判断一段心音是否异常。保持卷积神经网络（CNN）结构一致，仅改变音频转图像的前处理方式，对比三种方案：标准logmel谱图、时间归一化频带的PCEN，以及多分辨率堆叠谱图。在PhysioNet 2016数据集上，三者敏感度均约0.95，但PCEN（0.915）与多分辨率（0.916）在官方指标上优于普通logmel（0.910）。Grad-CAM分析显示模型注意力集中在低频区域，对应S1和S2心音位置，表明学习到有效特征。
原文摘要 · Abstract (English)
Heart disease kills a lot of people, and one cheap way to catch it early is by listening to heart sounds with a stethoscope, or better yet, just recording them and running them through a model. This project is a binary classification task: take a short clip of someones heartbeat and decide if it sounds normal or abnormal. Instead of trying out a bunch of different models, we kept the CNN the same the whole time and just changed how we turned the raw audio into a picture for it to look at. We tried three ways of doing that: a regular logmel spectrogram, PCEN (which basically normalizes each frequency bin over time), and a multi resolution version that stacks a few different window sizes together. We ran all three on the PhysioNet 2016 heart-sound dataset with the exact same setup but same model, same optimizer, same random seed. Turns out all three do pretty well at catching abnormal cases (sensitivity around 0.95), but PCEN and multi-resolution both edge out the plain logmel on the official PhysioNet accuracy metric (0.915 and 0.916 vs. 0.910). We also ran Grad-CAM to see where the model was actually looking, and it mostly focused on the low frequencies where S1 and S2 heart sounds live, which is a good sign that it learned something real
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。