Recognising Covid-19 from Coughing using Ensembles of SVMs and LSTMs with Handcrafted and Deep Audio Features
Interspeech 2021
; : 911-915, 2021.
Article
in English
| Web of Science | ID: covidwho-2044307
ABSTRACT
As the Covid-19 pandemic continues, digital health solutions can provide valuable insights and assist in diagnosis and prevention. Since the disease affects the respiratory system, it is hypothesised that sound formation is changed, and thus, an infection can be automatically recognised through audio analysis. We present an ensemble learning approach used in our entry to Track 1 of the DiCOVA 2021 Challenge, which aims at binary classification of Covid-19 infection on a crowd-sourced dataset of 1 040 cough sounds. Our system is based on a combination of hand-crafted features for paralinguistics with deep feature extraction from spectrograms using pre-trained CNNs. We extract features both at segment level and with a sliding window approach, and process them with SVMs and LSTMs, respectively. We then perform least-squares weighted late fusion of our classifiers. Our system surpasses the challenge baseline, with a ROC-AUC on the test set of 78.18 %.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
Interspeech 2021
Year:
2021
Document Type:
Article
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