Child Cry Classification - An Analysis of Features and Models

This paper presents a study on the classification of child cries based on various features extracted through speech and auditory processing. Certain spectral and descriptive features vary significantly in a child's cry intended for a specific purpose. Firstly, the model was trained using individual features. Later, the best features were selected and the model was again trained by combining these features. Logistic regression, SVM, KNN and Random Forest models were used for classification. A total of 457 samples were used for training/testing the models from the dataset Donate-a-cry corpus.