  We propose an approach of graph convolutional networks for robust infant cry
classification. We construct non-fully connected graphs based on the
similarities among the relevant nodes in both supervised and semi-supervised
node classification with convolutional neural networks to consider the
short-term and long-term effects of infant cry signals related to inner-class
and inter-class messages. The approach captures the diversity of variations
within infant cries, especially for limited training samples. The effectiveness
of this approach is evaluated on Baby Chillanto Database and Baby2020 database.
With as limited as 20% of labeled training data, our model outperforms that of
CNN model with 80% 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.
