Automatic Infant Cry Pattern Classification for a Multiclass Problem

Crying is the only way of communication for infants to express their physical and emotional needs. Automatic infant cry analysis that provides fast and non-invasive process is suitable to assess the physical and emotional states of infants. The cry analysis provides an opportunity to understand infants’ needs. It is also beneficial in clinical environment for identifying specific pathologies through infant cry. This paper presents an automatic infant cry classification system for a multiclass problem. The cry classification system consists of three stages: (1) feature extraction, (2) feature selection, and (3) pattern classification. We extracted spectral features, such as Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) to represent the acoustic characteristics of the cry signals. In addition, the combination of spectral and dynamic features was also investigated. Due to the high dimensionality of data resulting from the feature extraction stage, we selected relevant features to perform feature selection to reduce the data dimensionality. In this stage, five different feature selection techniques were experimented. In the pattern classification stage, two Artificial Neural Network (ANN) architectures: Multilayer Perceptron (MLP) and Radial Basis Function Network (RBFN) were used for classifying the cry signals into five categories: asphyxia, pain, hunger, deaf, and normal. Experimental results show that the best classification accuracy of 93.43% (Kappa value of 0.91) was obtained from MFCC + ∆MFCC + ∆∆MFCC feature set, when using CFS selection technique and RBFN