@inproceedings{2e2ef727527e4ec88098e83895158903,
title = "Denoising cough sound recordings using neural networks",
abstract = "Objective cough sound evaluation is useful in the diagnosis and management of respiratory diseases. However, the performance of cough sound analysis models can degrade in the presence of background noises common in everyday environments. This brings forward the need for cough sound denoising. This work utilizes a method for denoising cough sound recordings using signal processing and machine learning techniques, inspired by research in the field of speech enhancement. It uses supervised learning to find a mapping between the noisy and clean spectra of cough sound signals using a fully connected feed-forward neural network. The method is validated on a dataset of 300 manually annotated cough sound recordings corrupted with babble noise. The effect of various signal processing and neural network parameters on denoising performance is investigated. The method is shown to improve cough sound quality and intelligibility and outperform conventional denoising methods.",
author = "Laya Jose and Shlomo Berkovsky and Hao Xiong and Cecilia Mascolo and Sharan, \{Roneel V.\}",
year = "2023",
month = dec,
day = "11",
doi = "10.1109/EMBC40787.2023.10340687",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "2023 45th Annual International Conference of the IEEE Engineering in Medicine \& Biology Conference (EMBC)",
address = "United States",
note = "Annual International Conference of the IEEE Engineering in Medicine and Biology Conference (45th : 2023), EMBC 2023 ; Conference date: 24-07-2023 Through 27-07-2023",
}