Classify Respiratory Abnormality in Lung Sounds Using STFT and a Fine-Tuned ResNet18 Network

Chen, Zizhao; Wang, Hongliang; Yeh, Chia-Hui; Li, Xilin

doi:10.1109/biocas54905.2022.9948614

Cited by 18 publications

(2 citation statements)

References 17 publications

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“…It not only promotes non-contact evaluation of respiratory health but can also be included in smart home systems for remote health monitoring of patients in need of such medical support [26], [27]. As respiratory sounds are one of the first indications of respiratory illness, respiratory sound analysis can help with early detection and diagnosis of disease [1], [28], [29].…”

Section: Figure 1 Audio Processing Flowchartmentioning

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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Section: Figure 1 Audio Processing Flowchartmentioning

confidence: 99%

“…This study used ResNet18, a deep CNN model that was pre-trained on the ImageNet dataset [29], [106]. This is a residual network model with 18 layers, which is one of the smaller ResNet models.…”

Section: Deep Learning Modelmentioning

confidence: 99%

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

Huq

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Cross-Model Knowledge Distillation and Metadata Fusion for Respiratory Sound Classification

Sun,

Huang,

et al. 2024

Communications in Computer and Information Science

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Transfer learning for the efficient detection of COVID-19 from smartphone audio data

Campana

Delmastro

Pagani

2023

Pervasive and Mobile Computing

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Classify Respiratory Abnormality in Lung Sounds Using STFT and a Fine-Tuned ResNet18 Network

Cited by 18 publications

References 17 publications

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

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

Cross-Model Knowledge Distillation and Metadata Fusion for Respiratory Sound Classification

Transfer learning for the efficient detection of COVID-19 from smartphone audio data

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