COVID-19 Detection in Cough Audio Dataset Using Deep Learning Model

Sabet, Marziyeh; Ramezani, Amin; Ghasemi, Seyedeh Maryam

doi:10.1109/iccia54998.2022.9737168

Cited by 5 publications

(2 citation statements)

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“…Moreover, audio analysis can enhance the efficiency and accuracy of non-contact healthcare visits by automating certain aspects of the diagnosis process [51], [58]. Through machine learning and deep learning techniques, audio analysis algorithms can be trained on large datasets to recognize patterns, classify medical conditions, and provide decision support to healthcare professionals [1], [20], [32], [60], [61]. This automated analysis can help streamline the diagnosis process, enabling healthcare providers to make informed decisions more efficiently and effectively [3], [51], [62].…”

Section: Non-contact Medical Assessmentmentioning

confidence: 99%

“…With the spread of the COVID-19 pandemic, the research community put a large emphasis on contactless-cough-classification, and it was clear the technology had to be robust, generalizable to a global scale, fast with predictions, and indifferent to various types of recording devices, patient types/demographics, and environmental influences [19], [61], [94].…”

Section: Cough Sound Classificationmentioning

confidence: 99%

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Differentiation of Dry and Wet Cough Sounds using A Deep Learning Model and Data Augmentation

Huq

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Automated classification of cough sounds has increased in importance due to factors such as the worldwide COVID-19 pandemic. To train such classification models requires a large dataset of cough sounds; however, it remains challenging to find sufficient expert-labelled training data. This thesis explores a novel form of audio data augmentation, where training cough sounds are corrupted with varying levels of reverberation and Gaussian noise. The combination of noise and reverberation is more effective than both traditional image-based augmentation techniques and either noise or reverberation alone, leading to near-human accuracy on a wet vs. dry cough classification task using a ResNet18 model across two cough datasets. Alignment between the training and testing environments is examined using the Speech-Commands audio dataset. While models trained with the closest reverb and noise level to the test environment gave the best results, the proposed audio augmentation technique produces models with robust performance across test environments.Finally, I would like to thank my family -your unconditional love and encouragement has been the fuel that powered me through this experience. To my wife, Fariha, thank you for your love and patience, especially the last months of the program. To my sister and family, appi (Rumpa), cb (Tinku), Rehan & Faraz, thank you for always being there to cheer me up and encourage me every time I needed it. To my mother and father, thank you for your love, kindness, patience, and your belief in me.v Table of ContentsCopyright .

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