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Parallel source separation system for heart and lung sounds


In this paper, we propose a parallel source separation system designed to extract heart and lung sounds from single-channel mixtures. The proposed system is based on a non-negative matrix factorization (NMF) approach and a clustering strategy together with a soft-masking filtering. Furthermore, we propose an offline and online implementation of the framework which can be applied in many real-time scenarios, such as the extraction of clinical parameters, remote auscultation and breath sound analysis. Experimental results show that it is possible to achieve fast execution times, which enable a real-time behavior, combining parallel and high-performance techniques.

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This work was supported by the Regional Ministry of the Principality of Asturias under Grant FC-GRUPIN-IDI/2018/000226, by the Ministry of Economy, Knowledge and University of the Government of the Junta de Andalucía under Project P18-RT-1994 and by the “Programa Operativo FEDER Andalucía 2014-2020” under Project with Reference 1257914.

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Correspondence to A. J. Muñoz-Montoro.

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Muñoz-Montoro, A.J., Suarez-Dou, D., Cortina, R. et al. Parallel source separation system for heart and lung sounds. J Supercomput 77, 8135–8150 (2021).

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  • Non-negative matrix factorization (NMF)
  • Heart
  • Lung
  • Real time
  • Parallel computing
  • Sound source separation
  • Single-channel