An ambient denoising method based on multi-channel non-negative matrix factorization for wheezing detection

In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix factorization (NMF) approach and a detection strategy. More...

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Bibliographic Details
Published in:The Journal of supercomputing 2023-02, Vol.79 (2), p.1571-1591
Main Authors: Muñoz-Montoro, Antonio J., Revuelta-Sanz, Pablo, Martínez-Muñoz, Damian, Torre-Cruz, Juan, Ranilla, José
Format: Article
Language:English
Subjects:
Algorithms
Compilers
Computer Science
Factorization
Interpreters
Noise reduction
Processor Architectures
Programming Languages
Singular value decomposition
Sound sources
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Summary:In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix factorization (NMF) approach and a detection strategy. Moreover, the initialization of the proposed model is based on singular value decomposition to avoid dependence on the initial values of the NMF parameters. Additionally, novel update rules to simultaneously address the multichannel denoising while preserving an orthogonal constraint to maximize source separation have been designed. The proposed system has been evaluated for the task of wheezing detection showing a significant improvement over state-of-the-art algorithms when noisy sound sources are present. Moreover, parallel and high-performance techniques have been used to speedup the execution of the proposed system, showing that it is possible to achieve fast execution times, which enables its implementation in real-world scenarios.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04706-x