Croup and pertussis cough sound classification algorithm based on channel attention and multiscale Mel-spectrogram

Croup and pertussis are major illnesses that result in human fatality, especially in pediatric patients. Timely and accurate diagnosis of these diseases is crucial to reducing mortality rates. Therefore, there is a need for a lowcost, rapid, and accurate diagnostic solution. This paper proposes a croup and pertussis cough sound classification algorithm based on channel attention and multiscale Mel-spectrogram (CPCSC). Firstly, an automatic croup and pertussis cough classification method is implemented. Secondly, an adaptive scale audio feature extraction method (ASFE) is proposed, which is used to compute different scales of window sizes and hop length to generate the multiscale Mel-spectrogram (MSMel-spectrogram). Thirdly, a CNN model with a channel attention mechanism is proposed to extract features of the MSMel-spectrogram. The channel attention mechanism captures channel information to enhance model performance. Finally, the comparison results with six methods on the cough dataset, CSC4, demonstrate that CPCSC outperforms other comparison algorithms with an average accuracy, sensitivity, specificity, precision, and F1-score of 90.5%, 90.5%, 93.92%, 91.37%, and 90.25%, respectively.