A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification - Joshi, Vinayak Ravi; Srinivasan, Kathiravan; Vincent, P. M. Durai Raj; Rajinikanth, Venkatesan; Chang, Chuan-Yu (Frontiers in Public Health, 2022)
Understanding the reason for an infant’s cry is the most difficult thing for the parents. There might be various reasons behind the baby’s cry. It may be due to hunger, pain, sleep or diaper related problems. The key concept behind identifying the reason behind the infant’s cry is mainly based on the varying patterns of the cry audio. The audio file comprises many features, which are highly important in classifying the results. It is important to convert the audio signals into the required spectrograms. In this paper, we are trying to find efficient solutions to the problem of predicting the reason behind an infant’s cry. In this paper, we have used the MFCC algorithm to generate the spectrograms and analyzed the varying feature vectors. We then came up with two approaches to get the experimental results. In the first approach, we used the CNN variants like VGG16, YOLOv4 to classify the infant cry signals. In the second approach, a Multi-Stage Heterogeneous Stacking Ensemble Model was used for infant cry classification. Its major advantage was the inclusion of various advanced boosting algorithms at various levels. The proposed Multi-Stage Heterogeneous Stacking Ensemble Model had an edge over the other neural network models, especially in terms of overall performance and computing power. Finally, after many comparisons, the proposed model revealed virtuoso performance and a mean classification accuracy of up to 93.7%.