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Auscultation for neonates is a simple and non-invasive method of providingdiagnosis for cardiovascular and respiratory disease. Such diagnosis oftenrequires high-quality heart and lung sounds to be captured during auscultation.However, in most cases, obtaining such high-quality sounds is non-trivial dueto the chest sounds containing a mixture of heart, lung, and noise sounds. Assuch, additional preprocessing is needed to separate the chest sounds intoheart and lung sounds. This paper proposes a novel deep-learning approach toseparate such chest sounds into heart and lung sounds. Inspired by theConv-TasNet model, the proposed model has an encoder, decoder, and maskgenerator. The encoder consists of a 1D convolution model and the decoderconsists of a transposed 1D convolution. The mask generator is constructedusing stacked 1D convolutions and transformers. The proposed model outperformsprevious methods in terms of objective distortion measures by 2.01 dB to 5.06dB in the artificial dataset, as well as computation time, with at least a17-time improvement. Therefore, our proposed model could be a suitablepreprocessing step for any phonocardiogram-based health monitoring system.

Real-time Neonatal Chest Sound Separation using Deep Learning