Infant Cry Classification with Graph Convolutional Networks
Chunyan Ji, Ming Chen, Bin Li, Yi Pan
Abstract
We propose an approach of graph convolutional networks for robust infant cry classification. We construct non-fully connected graphs with weighted edges based on the similarities among the relevant nodes and feed them into convolutional neural networks to consider the short-term and long-term effects of infant cry signals related to inner-class and inter-class effects. The approach captures the diversity of variations within infant cries and gives encouraging results in both supervised and semi-supervised node classification. The effectiveness of this approach is evaluated on Baby Chillanto database and Baby2020 database. With limited 20% of labeled training data, our model outperforms the CNN model with 80% of labeled training data and the accuracy stably improves as the number of labeled training samples increases. The best results give significant improvements of 7.36% and 3.59% compared with the results of the CNN models on Baby Chillanto database and Baby2020 database, respectively.