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