Tomaszewska, Julia Z., Chousidis, Christos ORCID: https://orcid.org/0000-0003-3762-8208 and Donati, Eugenio ORCID: https://orcid.org/0000-0002-0048-1858 (2022) Sound-Based Cough Detection System using Convolutional Neural Network. In: 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 22-24 Jun 2022, Messina, Italy.

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Abstract

Sound recording and processing techniques can be used
in designing diagnostic solutions for a variety of medical
conditions related to the respiratory system. In this spectrum,
cough monitoring for chronic or seasonal conditions is a
significant medical practice. In this paper, a precise cough
identification and monitoring system is presented. The system is
utilising a convolutional neural network as a feature extraction
algorithm and classification system. Including several functions of
loading the audio data into the system and converting it into a set
of spectrograms, as well as the pre-segmentation stage function,
the model retains its relatively low-complexity, which allows
accelerating the learning process, also enhanced using dropout.
Due to limited audio data available, the dataset dimension was
established at 600 samples, split into two equal-numbered groups
– 300 samples of “cough” samples, and 300 of “non-cough”
samples. The validation accuracy (thus the percentage of samples
labelled correctly by the system during the validation process)
yielded over 84%, suggesting that this can be a successful cough
detection method for future medical applications and devices, such
as potential respiratory system condition diagnostic tool.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Computing
Depositing User:	Marc Forster
Date Deposited:	16 Mar 2024 08:13
Last Modified:	16 Mar 2024 08:13
URI:	https://repository.uwl.ac.uk/id/eprint/11310

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