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Cough endpoint detection in continuous audio recordings
Thesis (MEng)--Stellenbosch University, 2026.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2026
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| _version_ | 1867613732530552832 |
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| access_status_str | Open Access |
| author | Lamprecht, Christiaan Gerrit |
| author2 | Niesler, Thomas |
| author_browse | Lamprecht, Christiaan Gerrit Niesler, Thomas |
| author_facet | Niesler, Thomas Lamprecht, Christiaan Gerrit |
| author_sort | Lamprecht, Christiaan Gerrit |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2026. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/136216 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:49.380Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/136216 Cough endpoint detection in continuous audio recordings Lamprecht, Christiaan Gerrit Niesler, Thomas Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Thesis (MEng)--Stellenbosch University, 2026. Lamprecht, C. G. 2026. Cough endpoint detection in continuous audio recordings. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/0042b513-add8-47be-a51f-2a8b754b3582 Cough detection and monitoring plays an important role in the assessment of respiratory conditions in patients. Cough audio can also prove valuable information for models used in the classification and diagnosis of diseases. However, cough detection remains a challenging problem due to variables in audio data often found in environments where cough recordings are captured, as well as the need for disease diagnosis in areas with limited access to internet and devices capable of running deep neural network (DNN) models. This thesis investigates cough endpoint detection using all aspects of machine learning development in an attempt to produce a small DNN model suitable for use in potential mobile applications. The work covers custom dataset design with variations of signal-to-noise ratios, feature extraction to generate both linear and mel frequency spectrograms, low parameter count model design, hyperparameter optimization and finally a detailed evaluation on the achieved performance. Four main classifier architectures are considered including, logistic regression (LR), multilayer perceptron (MLP) and two convolutional neural networks (CNN) with a focus on minimizing model depth and parameter count while maintaining robust cough boundary detection capabilities. Models are trained and evaluated on separate datasets using conventional metrics to assess performance on a classification level, as well as alignment based evaluation methods to assess performance between predicted outputs and ground truth annotations. Hyperparameter optimization identifies optimal parameters among 7290 possible combinations for each classifier. Resulting in optimal models achieving an area under receiver operating characteristic (ROC) curve (AUC) of 0.921, 0.988, 0.985 and 0.992 for LR, MLP, two dimensional CNN and three dimensional CNN respectively. Final testing is done through simulating real world conditions, which presented complications with the original dataset and classifier. After the implementation of post classification processing, final performance achievements showed the classifier detected 99.7% of coughs while 3% of detected coughs were false positives, with false positives primarily caused by audio associated with or similar to coughs sounds. The final results presented a feasible and accurate cough endpoint detection system using compact neural network architecture and provides a strong basis for further research into cough boundary detection and future mobile development. Masters 2026-04-28T09:59:12Z 2026-04-28T09:59:12Z 2026-03 Thesis https://scholar.sun.ac.za/handle/10019.1/136216 en Stellenbosch University 97 pages : ill. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Lamprecht, Christiaan Gerrit Cough endpoint detection in continuous audio recordings |
| title | Cough endpoint detection in continuous audio recordings |
| title_full | Cough endpoint detection in continuous audio recordings |
| title_fullStr | Cough endpoint detection in continuous audio recordings |
| title_full_unstemmed | Cough endpoint detection in continuous audio recordings |
| title_short | Cough endpoint detection in continuous audio recordings |
| title_sort | cough endpoint detection in continuous audio recordings |
| url | https://scholar.sun.ac.za/handle/10019.1/136216 |
| work_keys_str_mv | AT lamprechtchristiaangerrit coughendpointdetectionincontinuousaudiorecordings |