用大模型提升心肺音分离效果，让听诊更准
Large Language Model-based Nonnegative Matrix Factorization For Cardiorespiratory Sound Separation
- 大模型提供疾病诊断线索，优化分离过程
- 在210组临床模拟数据上表现优于现有方法
- 适合医疗音频分析与智能听诊研发者
本研究首次将大语言模型（LLM）与非负矩阵分解（NMF）结合，开创性地推动了声源分离领域的发展。LLM以两种方式发挥作用：一是通过提供疾病预测的详细信息提升分离质量；二是作为反馈机制，优化添加到NMF代价函数中的基频惩罚项。实验在两个数据集上进行：100组由真实测量值合成的混合信号，以及210组来自临床模拟人体的的心肺音录音，包含单音和混合音，使用数字听诊器采集。该方法在所有测试中均优于现有技术，展现出显著提升医学声音分析用于疾病诊断的潜力。
原文摘要 · Abstract (English)
This study represents the first integration of large language models (LLMs) with non-negative matrix factorization (NMF), marking a novel advancement in the source separation field. The LLM is employed in two unique ways: enhancing the separation results by providing detailed insights for disease prediction and operating in a feedback loop to optimize a fundamental frequency penalty added to the NMF cost function. We tested the algorithm on two datasets: 100 synthesized mixtures of real measurements, and 210 recordings of heart and lung sounds from a clinical manikin including both individual and mixed sounds, captured using a digital stethoscope. The approach consistently outperformed existing methods, demonstrating its potential to significantly enhance medical sound analysis for disease diagnostics.
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