DenseNet-CBAM for Identifying Damp-Heat Constitution and Balanced Constitution by Human Voice

Body constitution of traditional Chinese medicine(TCM) can be used to guide the prevention, diagnosis, treatment, rehabilitation and health preservation of diseases. At present, the identification of damp-heat constitution and balanced constitution is mostly determined by questionnaire, which leads to the great influence of subjective factors. Therefore, we propose an effective classification model DenseNet-CBAM for identifying damp-heat constitution and balanced constitution, which can help doctors objectively identify TCM constitution. By adding the Convolutional Attention Block Module (CBAM) to the DenseNet network, the feature extraction ability of the network can be effectively improved. We preprocess 700 voice data of 34 subjects to obtain the corresponding Mel chromatograms, and use ImageNet and AudioSet pre-trained DenseNet-CBAM model to classify them. The accuracy of our method is 82.69 %, which higher than AlexNet, ResNet and DenseNet, respectively. It can be seen that our method can improve the efficiency of constitution identification and promote the objectification of constitution identification.