Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

Cohen-McFarlane, Madison; Xi, Pengcheng; Wallace, Bruce; Habashy, Karim; Huq, Saif; Goubran, Rafik; Knoefel, Frank

doi:10.1109/ojemb.2022.3202435

Cited by 5 publications

(13 citation statements)

References 44 publications

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“…In the medical field, audio analysis has shown great promise in a variety of applications. One notable area is respiratory sound analysis, where the acoustic characteristics of cough sounds, breathing sounds, and lung sounds can be analyzed to diagnose respiratory conditions, such as asthma, chronic obstructive pulmonary disease (COPD), or pneumonia [15], [16]. Additionally, cardiac auscultation is a process that involves analyzing heart sounds and murmurs to detect cardiovascular abnormalities and assess cardiac health [17], [18].…”

Section: Background and Motivationmentioning

confidence: 99%

“…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%

“…Additionally, the vocal cords may also contribute to the quality and tone of the cough sound, depending on the specific condition or underlying cause [15], [38].…”

Section: Figure 2 the Human Respiratory Systemmentioning

confidence: 99%

“…Coughs can be broadly classified into two main types: wet and dry coughs. These categories are based on the nature of the cough sound and the underlying conditions that trigger them [15], [48]. Distinguishing between wet and dry coughs is essential for accurate diagnosis and appropriate treatment of respiratory ailments [35], [49].…”

Section: Cough Typesmentioning

confidence: 99%

“…Common triggers for a dry cough include viral infections like the common cold or influenza, exposure to environmental irritants such as smoke or dust, allergies, or even certain medications. Unlike a wet cough, a dry cough may not provide immediate relief to the irritation in the airways since there is no mucus to be cleared [2], [15]. The lack of mucus in the cough sound can give it a "hacking" or "itchy"…”

Section: Cough Typesmentioning

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: Background and Motivationmentioning

confidence: 99%

Section: Figure 1 Audio Processing Flowchartmentioning

confidence: 99%

“…Additionally, the vocal cords may also contribute to the quality and tone of the cough sound, depending on the specific condition or underlying cause [15], [38].…”

Section: Figure 2 the Human Respiratory Systemmentioning

confidence: 99%

Section: Cough Typesmentioning

confidence: 99%

Section: Cough Typesmentioning

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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Deep Convolutional Neural Networks for the Classification and Detection of Human Vocal Exclamations of Panic in Subway Systems

Huang

Mushi

2023

IEEE Access

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The automated classification and detection of vocal exclamations of panic made by human beings in subway systems can enable more effective emergency response. Thus, in this study, we designed four multiscale deep convolutional neural networks (models 1-4) with one-and two-dimensional layers for detecting and classifying vocal exclamations of panic. First, we applied a decision-making framing-padding algorithm formulated to preprocess vocal exclamations of panic. Vocal sounds were then mixed with noise signals. Mel spectrogram, log-Mel spectrogram, and signal waveform data were used as learning data. The implementation of an ensemble technique in model 1 improved classification performance by 0.25% and 0.75% in terms of the F1 score at signal-to-noise ratios (SNRs) of 15 and −15, respectively. Models 4 and 2 exhibited the best classification performance and achieved F1 scores of 99.74% (under SNR = 15) and 80.56% (under SNR = -15), respectively. Model 2 performed the best in detecting screaming, quarrelling, and loud talking when SNR = 15 (F1 scores of 94.59%, 49.06%, and 64.94%, respectively). Model 2 also performed the best in distinguishing screaming and non-screaming. Our models outperformed their state-of-the-art counterparts in detection and classification at SNRs of 15 and 10.

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Data Augmentation and Deep Learning in Audio Classification Problems: Alignment Between Training and Test Environments

Huq,

Xi,

Goubran

et al. 2023

2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)

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Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

Cited by 5 publications

References 44 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

Deep Convolutional Neural Networks for the Classification and Detection of Human Vocal Exclamations of Panic in Subway Systems

Data Augmentation and Deep Learning in Audio Classification Problems: Alignment Between Training and Test Environments

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