Wavelet-Based Cough Signal Decomposition for Multimodal Classification

Signal classifications have benefited from the successes of ML and DNN architectures. Cough classification techniques mainly extract features such as the Mel Frequency Cepstral Coefficients for training. Most of these works also focus on obtaining information from single data modalities. However, multimodal analysis has been shown to aggregate useful information from different modalities thereby improving the internal capacity of ML models at data analysis. In this research, we propose a multimodal cough data classification approach with scalograms images obtained by decomposing cough signals using continuous wavelet transform and clinical information of subjects obtained from the COUGHVID dataset. Our result shows improved precision as compared to expert analysis.