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arXiv · 2502.05757

Large Language Model-based Nonnegative Matrix Factorization For Cardiorespiratory Sound Separation

Yasaman Torabi·Shahram Shirani·James P. Reilly
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Abstract

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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BibTeXRIS

Yasaman Torabi, Shahram Shirani, James P. Reilly. 2025-02-09. Large Language Model-based Nonnegative Matrix Factorization For Cardiorespiratory Sound Separation. https://arxiv.org/abs/2502.05757

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