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A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation

A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation

The width of the window used was 0.96 seconds, while the window stride length was equal to half of the window’s width (0.48 s). This setting resulted in a mel spectrogram segment with a size of 64 mel bins × 96 frames. To facilitate model training, the COVID-19 Sounds and Coswara datasets were partitioned based on chronological order into a development set and a postdevelopment set by applying a 70:30 ratio.

Theofanis Ganitidis, Maria Athanasiou, Konstantinos Mitsis, Konstantia Zarkogianni, Konstantina S Nikita

J Med Internet Res 2025;27:e66919