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

arXiv: https://arxiv.org/abs/2502.05757 · HTML: https://mlsanity.com/paper/2502.05757/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance](https://mlsanity.com/paper/2510.03906/) (2025)
- [GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing](https://mlsanity.com/paper/2510.20266/) (2025)
- [Chem-NMF: Multi-layer α-divergence Non-Negative Matrix Factorization for Cardiorespiratory Disease Clustering, with Improved Convergence Inspired by Chemical Catalysts and Rigorous Asymptotic Analysis](https://mlsanity.com/paper/2510.06632/) (2025)
- [QuPCG: Quantum Convolutional Neural Network for Detecting Abnormal Patterns in PCG Signals](https://mlsanity.com/paper/2511.02140/) (2025)

## Abstract (arXiv)

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.