Can Machine Learning Be Used to Recognize and Diagnose Coughs?

@article{Bales2020CanML,
  title={Can Machine Learning Be Used to Recognize and Diagnose Coughs?},
  author={Charles Bales and Charles N. John and Hasan Farooq and Usama Masood and Muhammad Nabeel and Ali Shariq Imran},
  journal={2020 International Conference on e-Health and Bioengineering (EHB)},
  year={2020},
  pages={1-4}
}
Emerging wireless technologies, such as 5G and beyond, are bringing new use cases to the forefront, one of the most prominent being machine learning empowered health care. One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections. Since cough is an essential symptom of many respiratory infections, an automated system to screen for respiratory diseases based on raw cough data would have a multitude of beneficial research and medical… 

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References

SHOWING 1-10 OF 29 REFERENCES
ESC: Dataset for Environmental Sound Classification
TLDR
A new annotated collection of 2000 short clips comprising 50 classes of various common sound events, and an abundant unified compilation of 250000 unlabeled auditory excerpts extracted from recordings available through the Freesound project are presented.
Deep Neural Networks for Identifying Cough Sounds
TLDR
Experimental results show both network architectures outperform traditional methods for cough detection and the effect of the network size parameters and the impact of long-term signal dependencies in cough classifier performance is explored.
Adam: A Method for Stochastic Optimization
TLDR
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Freesound technical demo
TLDR
This demo wants to introduce Freesound to the multimedia community and show its potential as a research resource.
A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children
TLDR
The results indicate that this technology has a role as a high-level diagnostic aid in the assessment of common childhood respiratory disorders, including asthma, pneumonia and bronchiolitis.
Automatic Croup Diagnosis Using Cough Sound Recognition
TLDR
This paper aims to develop automated cough sound analysis methods to objectively diagnose croup, and proposes the use of mathematical features inspired by the human auditory system, including the cochleagram for feature extraction and mel-frequency cepstral coefficients to capture the relevant aspects of the short-term power spectrum of speech signals.
Comparison and Analysis of SampleCNN Architectures for Audio Classification
TLDR
SampleCNN is scrutinized further by comparing it with spectrogram-based CNN and changing the subsampling operation in three different audio domains and shows that the excitation in the first layer is sensitive to the loudness, which is an acoustic characteristic that distinguishes different genres of music.
Deep Convolutional Neural Network with Mixup for Environmental Sound Classification
TLDR
A novel deep convolutional neural network is proposed to be used for environmental sound classification (ESC) tasks that uses stacked Convolutional and pooling layers to extract high-level feature representations from spectrogram-like features.
...
1
2
3
...