发表机构
McMaster University; Bell Canada（麦克马斯特大学; 贝尔加拿大公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出XVAE-WMT算法，无需配对干净心肺音记录，结合VAE、XAI与CWT前端，经SHAP降维后在两个数据集上实现26.8 dB SDR等优异分离性能，提升心肺音盲源分离效果。
AI 中文摘要
心血管声音分离是生物医学信号处理中的关键任务。本文提出XVAE-WMT1，一种无监督可解释生成AI算法，结合变分自编码器（VAE）、可解释AI（XAI）、基于小波的输入、事后输出掩码及时间一致性（TC）损失。与现有依赖短时傅里叶变换（STFT）且忽略潜在可解释性的监督及基于VAE的方法不同，XVAE-WMT无需配对干净记录，集成连续小波变换（CWT）前端以实现更优时频定位。我们通过不同指标评估潜在空间可解释性，SHAP（SHapley加性解释）可将潜在特征降维至前75%同时保留分离质量。在两个数据集上采用信号失真比（SDR）、信号干扰比（SIR）及信号伪影比（SAR）评估，XVAE-WMT达到26.8 dB SDR、32.8 dB SIR及28.6 dB SAR。
英文摘要
The separation of cardiovascular sounds is a critical task in biomedical signal processing. In this paper, we introduce XVAE-WMT1, an unsupervised explainable generative AI algorithm combining a variational autoencoder (VAE) with explainable AI (XAI), wavelet-based inputs, a post-hoc output mask, and temporal consistency (TC) loss. Unlike existing supervised and VAE-based methods that rely on Short-Time Fourier Transform (STFT) and ignore latent interpretability, XVAE-WMT requires no paired clean recordings and integrates a Continuous Wavelet Transform (CWT) front-end for superior time-frequency localization. We assessed the latent space interpretability via different metrics, with SHAP (SHapley Additive exPlanations) enabling dimensionality reduction to the top 75% of latent features while preserving separation quality. Evaluated across two datasets using Signal-to-Distortion Ratio (SDR), Signal-to-Interference Ratio (SIR), and Signal-to-Artifacts Ratio (SAR), XVAE-WMT attains 26.8 dB SDR, 32.8 dB SIR, and 28.6 dB SAR.
