Feature extraction algorithm fusing GFCC and phase information

Zhang, Yi; Ni, Lei

doi:10.1109/iaeac.2017.8054196

Cited by 5 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The COUGHVID dataset was recorded from Apr 1, 2020, in a Web application and deployed on a private server at the École Polytechnique Fédérale de Lausanne parameters during feature extraction from COUGHVID crowdsource cough dataset, i.e., first, the noise robustness feature vector as the undertaken problem is dependent on the public crowdsource dataset, and second, the feature vector should be highly correlated in nature. Consequently, Gammatone Frequency Cepstral Coefficients (GFCCs) were considered feature vectors during the extraction stage that will fulfill noise robustness and correlate to the objectives [22]. The detailed process of GFCC extraction from the COUGHVID crowdsource cough data set is as follows [23].…”

Section: The Research Objectives Arementioning

confidence: 99%

SRC: Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

Swain¹,

Khan²,

Chowdhary³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients (GFCC) for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network (DHO-ANN). The noisy crowdsourced cough datasets were collected from the public domain. This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora. The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures, F1 score, confusion matrix, specificity, and sensitivity parameters. Besides, it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset, COUGHVID. Moreover, the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5% compared to the existing methods.

show abstract

Section: The Research Objectives Arementioning

confidence: 99%