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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) |
|---|---|
| Identifier: | 10.1109/MeMeA54994.2022.9856512 |
| Identifier: | 10.1109/MeMeA54994.2022.9856512 |
| Subjects: | Computing |
| Related URLs: | |
| Depositing User: | Users 627 not found. |
| Date Deposited: | 16 Mar 2024 08:13 |
| Last Modified: | 04 Nov 2024 11:16 |
| URI: | https://repository.uwl.ac.uk/id/eprint/11310 |
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