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Cough Detection Challenge
Help push the frontier in cough detection research by improving the performance in Machine Learning-based cough classification!
Keywords: Deep Learning, Machine Learning, Cough Detection
Coughing is the most common complaint as to why individuals seek medical advice. In consequence, many efforts have been made towards creating an objective measure for cough.
To date, however, there is no standardized method, and there is no sufficiently validated generic automatic cough monitor that is commercially available and clinically acceptable.
The successful candidate will make her-/hisself familiar with the existing ML pipeline. Subsequently, the candidate will screen the literature to identify promising approaches. Finally, the candidate will optimize and evaluate these methods on one of the greatest cough datasets. Ideally, in a future step, this model will be deployed in form of an Android mobile application.
Coughing is the most common complaint as to why individuals seek medical advice. In consequence, many efforts have been made towards creating an objective measure for cough. To date, however, there is no standardized method, and there is no sufficiently validated generic automatic cough monitor that is commercially available and clinically acceptable.
The successful candidate will make her-/hisself familiar with the existing ML pipeline. Subsequently, the candidate will screen the literature to identify promising approaches. Finally, the candidate will optimize and evaluate these methods on one of the greatest cough datasets. Ideally, in a future step, this model will be deployed in form of an Android mobile application.
Beat the existing record on our database! By doing so develop a different approach and contribute to the state-of-art in cough detection.
Barata, Filipe, et al. "Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study." Journal of Medical Internet Research 22.7 (2020): e18082.
For the application, you should meet the following requirements: Experience in programming Python and TensorFlow (or equivalent). Experience in Deep/ Machine Learning. Experience with Audio/Acoustics and mobile technology is a plus. ETH / UZH students are preferred.
Beat the existing record on our database! By doing so develop a different approach and contribute to the state-of-art in cough detection.
Barata, Filipe, et al. "Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study." Journal of Medical Internet Research 22.7 (2020): e18082.
For the application, you should meet the following requirements: Experience in programming Python and TensorFlow (or equivalent). Experience in Deep/ Machine Learning. Experience with Audio/Acoustics and mobile technology is a plus. ETH / UZH students are preferred.
Please send your application with a short motivation letter (~250 words), transcript of records, and CV to Dr. Barata (fbarata@ethz.ch).
Please send your application with a short motivation letter (~250 words), transcript of records, and CV to Dr. Barata (fbarata@ethz.ch).