Binary Particle Swarm Optimization for selection of features in the recognition of infants cries with asphyxia

The infant cry signals with asphyxia have distinct patterns which can be recognized using pattern classifiers such as Artificial Neural Network (ANN). This study investigates the effect of selecting infant cry features using the Binary Particle Swarm Optimization on the performance of Multilayer Perceptron (MLP) classifier in discriminating between healthy and infants with asphyxia from cry signals. The feature extraction process was performed by MFCC analysis. The MLP classifier performance was examined using various combination of number of coefficients. It was found that the BPSO helps to enhance the classification accuracy of MLP classifier while reducing the computational load. The highest MLP classification accuracy achieved was 95.07%, which was obtained when 26 MFCC filter banks, 14 selected MFC coefficients and 5 hidden nodes were used.