Contrastive learning of cough descriptors for automatic COVID-19 preliminary diagnosis
22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
; 6:4281-4285, 2021.
Article
in English
| Scopus | ID: covidwho-1535022
ABSTRACT
Cough sounds as a descriptor have been used for detecting various respiratory ailments based on its intensity, duration of intermediate phase between two cough sounds, repetitions, dryness etc. However, COVID-19 diagnosis using only cough sounds is challenging because of cough being a common symptom among many non COVID-19 health diseases and inherent data imbalance within the available datasets. As one of the approach in this direction, we explore the robustness of multi-domain representation by performing the early fusion over a wide set of temporal, spectral and tempo-spectral handcrafted features, followed by training a Support Vector Machine (SVM) classifier. In our second approach, using a contrastive loss function we learn a latent space from Mel Filter Cepstral Coefficients (MFCCs) where representations belonging to samples having similar cough characteristics are closer. This helps learn representations for the highly varied COVID-negative class (healthy and symptomatic COVID-negative), by learning multiple smaller clusters. Using only the DiCOVA data, multi-domain features yields an absolute improvement of 0.74% and 1.07%, whereas our second approach shows an improvement of 2.09% and 3.98%, over the blind test and validation set, respectively, when compared with challenge baseline. Copyright © 2021 ISCA.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
Year:
2021
Document Type:
Article
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