# A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation

arXiv: https://arxiv.org/abs/2109.03275 · HTML: https://mlsanity.com/paper/2109.03275/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 23 references that are in the map.
- First posted on arXiv 4 Sep 2021.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era](https://mlsanity.com/paper/2301.09362/) (2023)
- [Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications](https://mlsanity.com/paper/2109.15127/) (2021)
- [Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds](https://mlsanity.com/paper/2201.03211/) (2022)
- [Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds](https://mlsanity.com/paper/2201.10105/) (2022)

## Abstract (arXiv)

Obtaining high-quality heart and lung sounds enables clinicians to accurately assess a newborn's cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation-based approach is proposed to separate noisy chest sound recordings into heart, lung, and noise components to address this problem. This method is achieved through training with 20 high-quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second noisy recordings containing both heart and lung sounds and compared to the current state of the art Non-negative Matrix Factorisation methods. Results show significant improvements in heart and lung sound quality scores respectively, and improved accuracy of 3.6bpm and 1.2bpm in heart and breathing rate estimation respectively, when compared to existing methods.