• Corpus ID: 236456536

Deep Transfer Learning based COVID-19 Detection in Cough, Breath and Speech using Bottleneck Features

  title={Deep Transfer Learning based COVID-19 Detection in Cough, Breath and Speech using Bottleneck Features},
  author={Madhurananda Pahar and Thomas R. Niesler},
We present an experimental investigation into the automatic detection of COVID-19 from coughs, breaths and speech as this type of screening is non-contact, does not require specialist medical expertise or laboratory facilities and can easily be deployed on inexpensive consumer hardware. Smartphone recordings of cough, breath and speech from subjects around the globe are used for classification by seven standard machine learning classifiers using leave-p-out cross-validation to provide a… 



COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings

An AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings, and provide a personalized patient saliency map to longitudinally monitor patients in real-time, non-invasively, and at essentially zero variable cost is developed.

A Comparative Study of Features for Acoustic Cough Detection Using Deep Architectures*

  • Igor MirandaA. DiaconT. Niesler
  • Computer Science
    2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2019
Although MFCC performance is improved by sinusoidal liftering, STFT and MFB lead to better results, an improvement exceeding 7% in the area under the receiver operating characteristic curve across all classifiers is achieved.

End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

It is shown that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings, and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital CO VID-19 diagnostic tool.

Deep Neural Network Based Cough Detection Using Bed-Mounted Accelerometer Measurements

It is concluded that high-accuracy cough monitoring based only on measurements from the accelerometer in a consumer smartphone is possible and may represent a more convenient and readily accepted method of long-term patient cough monitoring.

COVID-19 cough classification using machine learning and global smartphone recordings

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

The results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds, and opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid CO VID-19 diagnosis.

Coswara - A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis

The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription

A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels

The proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called ‘DeepCough’.

Automatic cough classification for tuberculosis screening in a real-world environment

The automatic classification of cough audio sounds, when applied to symptomatic patients requiring investigation for TB, can meet the WHO triage specifications for the identification of patients who should undergo expensive molecular downstream testing.

DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics

The challenge features two tracks, one focusing on cough sounds, and the other on using a collection of breath, sustained vowel phonation, and number counting speech recordings, and a baseline system for the task is presented.