用多通道非负矩阵分解降噪并检测喘鸣，适合临床实时应用。
An ambient denoising method based on multi-channel non-negative matrix factorization for wheezing detection
- 基于SVD初始化的NMF方法，提升模型稳定性。
- 多通道联合去噪同时保持正交性，分离效果更优。
- 支持并行计算，实现实时处理，适合真实听诊场景。
本文提出一种并行计算方法，用于从听诊过程中采集的多通道录音中同时进行背景噪声抑制和喘鸣检测。该系统基于非负矩阵分解（NMF）方法与检测策略，通过奇异值分解（SVD）初始化模型，降低对NMF参数初始值的依赖性。设计了新型更新规则，在实现多通道去噪的同时保留正交约束，以最大化源信号分离效果。实验表明，该系统在存在噪声干扰的情况下，相比现有最优算法显著提升了喘鸣检测性能。同时采用并行与高性能技术加速计算，实现了快速执行时间，具备在真实临床环境中部署的可行性。
原文摘要 · Abstract (English)
In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix factorization (NMF) approach and a detection strategy. Moreover, the initialization of the proposed model is based on singular value decomposition to avoid dependence on the initial values of the NMF parameters. Additionally, novel update rules to simultaneously address the multichannel denoising while preserving an orthogonal constraint to maximize source separation have been designed. The proposed system has been evaluated for the task of wheezing detection showing a significant improvement over state-of-the-art algorithms when noisy sound sources are present. Moreover, parallel and high-performance techniques have been used to speedup the execution of the proposed system, showing that it is possible to achieve fast execution times, which enables its implementation in real-world scenarios.
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