  Stethoscope-recorded chest sounds provide the opportunity for remote
cardio-respiratory health monitoring of neonates. However, reliable monitoring
requires high-quality heart and lung sounds. This paper presents novel
Non-negative Matrix Factorisation (NMF) and Non-negative Matrix
Co-Factorisation (NMCF) methods for neonatal chest sound separation. To assess
these methods and compare with existing single-source separation methods, an
artificial mixture dataset was generated comprising of heart, lung and noise
sounds. Signal-to-noise ratios were then calculated for these artificial
mixtures. These methods were also tested on real-world noisy neonatal chest
sounds and assessed based on vital sign estimation error and a signal quality
score of 1-5 developed in our previous works. Additionally, the computational
cost of all methods was assessed to determine the applicability for real-time
processing. Overall, both the proposed NMF and NMCF methods outperform the next
best existing method by 2.7dB to 11.6dB for the artificial dataset and 0.40 to
1.12 signal quality improvement for the real-world dataset. The median
processing time for the sound separation of a 10s recording was found to be
28.3s for NMCF and 342ms for NMF. Because of stable and robust performance, we
believe that our proposed methods are useful to denoise neonatal heart and lung
sound in a real-world environment. Codes for proposed and existing methods can
be found at: https://github.com/egrooby-monash/Heart-and-Lung-Sound-Separation.
