Dual tree complex Wavelet Packet Transform based infant cry classification

Abstract

A new method has been implemented based on Dual Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction for infant cry signal classification. The infant cry signals were decomposed into five levels using DT-CWPT. A total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band. Two classifiers Extreme Learning Machine (ELM) and Support Vector Machine (SVM) were used to classify the infant cry signal based on the extracted features. Three category of two-class experiments were conducted in this paper (asphyxia versus normal, hunger versus pain, and deaf versus normal). The results demonstrate that the DT-CWPT feature extraction and classification methods give a high accuracy of 97.87%, 87.26%, 100.00% for asphyxia versus normal, hunger versus pain, and deaf versus normal respectively.