On-device cough detection and respiratory disease classification enhanced by generative data augmentation.

BACKGROUND

Cough sounds are accessible, non-invasive biomarkers for respiratory disease assessment and can be captured using consumer-grade smartphones. Existing approaches typically focus solely on cough detection or rely on server-based deep learning for disease classification, which limits deployability and raises privacy concerns. Small, imbalanced cough datasets further hinder model generalization.

OBJECTIVE

To develop a multilayer, smartphone-compatible AI framework for automated cough detection and respiratory disease classification, and to propose a pioneering generative augmentation strategy utilizing a suite of five Variational Autoencoder (VAE) variants and a probabilistic cough-level fusion mechanism to improve disease classification under severe data scarcity and the limitations of conventional audio augmentation techniques.

METHODS

The proposed framework consists of three AI modules: (1) A Cough Detection Module (CDM) that performs real-time cough event detection and segmentation from continuous audio using lightweight models optimized for on-device execution. (2) A Disease Analysis Module (DAM) that classifies cough events into asthma, COVID-19, or healthy classes using parallel Support Vector Machine classifiers and a probabilistic cough-level fusion strategy. (3) A Generative Augmentation Module (GAM) employing five distinct VAE architectures. This module uniquely operates across the time-frequency domain for latent feature optimization while reconstructing samples in the time-domain to ensure acoustic verifiability.

RESULTS

The CDM provides reliable segmentation across heterogeneous recording conditions. The DAM achieves strong discriminability between asthma, COVID-19, and healthy coughs using compact cepstral and spectral features. The multi-variant GAM framework demonstrates superior efficacy in alleviating class imbalance specifically, the cross-domain (time-frequency to time-domain) reconstruction allows for the clinical verification of synthetic biomarkers. All system components operate in real time on commodity Android hardware.

CONCLUSION

This integrated framework addresses key limitations in dataset availability, model generalizability, and deployability, introducing an interpretable generative approach that demonstrates the feasibility of smartphone-based acoustic sensing as a scalable and privacy-preserving tool for respiratory health monitoring.