The Study of Learning System for Infant Cry Classification Using Discrete Wavelet Transform and Extreme Machine Learning

Abstract

The learning system of infant cry is presented. This system consists of characteristics attraction technique and classification technique. The characteristics attraction of infant cry are based on Discrete Wavelet Transform (DWT) methods. Whilst the sound classification of coefficients characteristics uses Single Layer Neural Feed Forward (SLNF) as an Extreme Learning Machine (ELM). The Dunstan Baby Language (DBL) is the sound database for the proposed system. The sound database was collected from infants between birth and 6 months of age. Where the baby language groups are categorized into 5 types: "Eh", "Eairh", "Neh", "Heh" and "Owh", respectively. The accuracy of sound classification was designated at the number of hidden nodes of 10 – 50 with a training and testing ratio of 70/30. The suitable results are based on the number of epochs, accuracy and performances. The results show that the average accuracy of all discrete wavelet functions on the baby language are over 80%. The average performance of Sym2 is suitable for all baby language groups. Moreover, the average number of epochs of Bior3.1 is suitable for all baby language groups.