Large Language Model-based Nonnegative Matrix Factorization For Cardiorespiratory Sound Separation
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913684674052096 |
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| author | Torabi, Yasaman Shirani, Shahram Reilly, James P. |
| author_facet | Torabi, Yasaman Shirani, Shahram Reilly, James P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model-based Nonnegative Matrix Factorization For Cardiorespiratory Sound Separation Torabi, Yasaman Shirani, Shahram Reilly, James P. Sound Audio and Speech Processing Signal Processing 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. |
| title | Large Language Model-based Nonnegative Matrix Factorization For Cardiorespiratory Sound Separation |
| topic | Sound Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2502.05757 |