COVID-19 Detection from Spectral features on the DiCOVA Dataset
(Oral presentation)
| Kotra Venkata Sai Ritwik (NITK Surathkal, India), Shareef Babu Kalluri (NITK Surathkal, India), Deepu Vijayasenan (NITK Surathkal, India) |
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In this paper we investigate the cues of COVID-19 on sustained phonation of Vowel-/i/, deep breathing and number counting data of the DiCOVA dataset. We use an ensemble of classifiers trained on different features, namely, super-vectors, formants, harmonics and MFCC features. We fit a two-class Weighted SVM classifier to separate the COVID-19 audio from Non-COVID-19 audio. Weighted penalties help mitigate the challenge of class imbalance in the dataset. The results are reported on the stationary (breathing, Vowel-/i/) and non-stationary (counting data) data using individual and combination of features on each type of utterance. We find that the Formant information plays a crucial role in classification. The proposed system resulted in an AUC score of 0.734 for cross validation, and 0.717 for evaluation dataset.
