Combination of Wavelets and Hard Thresholding for Analysis of Cough Signals

Taquee, Ahmad; Bhateja, Vikrant; Shankar, Adya; Srivastava, Agam

doi:10.1109/worlds4.2018.8611597

Cited by 8 publications

(2 citation statements)

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“…For identification of cough signals from noisy environments, the works of M. Payam, Et al [3] and S. Agam, Et al [7] were used to identify cough audios from a roo m with different white noise sources, an array of 7 microphones were used with delay-and-sum beamforming method. The distance between the microphones were varied to fine-tune the cough detection process and in conclusion, a cough detector with an array of microphone was found to be better than a single microphone cough detector for noisy environment [3].…”

Section: A Review Of the Literaturementioning

confidence: 99%

“…Although a different approach to extract cough audio fro m noisy environment would be by the combination of Hard Thresholding and Discreate Wavelet Transform (DWT). The 3-step process of discrete wavelet transform, hard thresholding and inverse discrete wavelet transform y ield in an audio free of white noise and improves the Signal-to-Noise Ratio (SNR) of the cough audio signals [7].…”

Section: A Review Of the Literaturementioning

confidence: 99%

See 1 more Smart Citation

Machine Learning based COVID-19 Cough Classification Models - A Comparative Analysis

Vrindavanam¹,

Srinath²,

Shankar³

et al. 2021

2021 5th International Conference on Computing Methodologies and Communication (ICCMC)

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COVID-19 continues to be a global pandemic and many a technological intervention are already in place for identification of COVID-19 patients. The paper focuses on the contactless detection of COVID-19 patients by analyzing their respective cough audio samples. The paper demonstrates three machine learning classification models and determines the better classifier among these three models. The model has made use of 15 dominant features. The paper has employed a method of selecting features based on ranking different scores derived from the feature selecting algorithms. The initial results will be forming part of a larger project of developing suitable interfaces, as such devices can reduce the stress on frontline workers and provide an efficient way to manage the resources and time of healthcare professionals. The proposed method has been tested on cough audios both COVID-19 positive and healthy individuals, and the results are promising.

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Section: A Review Of the Literaturementioning

confidence: 99%

Section: A Review Of the Literaturementioning

confidence: 99%