Infant Cry Recognition System: A Comparison of System Performance based on CDHMM and ANN

Infant Cry Recognition System: A Comparison of System Performance based on CDHMM and ANN

Yosra Abdulaziz Mohammed (University of Fallujah, Baghdad, Iraq)
DOI: 10.4018/IJAPUC.2019010102
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Cries of infants can be seen as an indicator of pain. It has been proven that crying caused by pain, hunger, fear, stress, etc., show different cry patterns. The work presented here introduces a comparative study between the performance of two different classification techniques implemented in an automatic classification system for identifying two types of infants' cries, pain, and non-pain. The techniques are namely, Continuous Hidden Markov Models (CHMM) and Artificial Neural Networks (ANN). Two different sets of acoustic features were extracted from the cry samples, those are MFCC and LPCC, the feature vectors generated by each were eventually fed into the classification module for the purpose of training and testing. The results of this work showed that the system based on CDHMM have better performance than that based on ANN. CDHMM gives the best identification rate at 96.1%, which is much higher than 79% of ANN whereby in general the system based on MFCC features performed better than the one that utilizes LPCC features.
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A number of research work related to this line have been reported, whereby many of which are based on Artificial Neural Network (ANN) classification techniques. (Petroni, Malowany, Johnston, & Stevens,1995) for example, have used three different varieties of supervised ANN technique which include a simple feed-forward, a recurrent neural network (RNN) and a time-delay neural network (TDNN) in their infant cry classification system. In their study, they have attempted to recognize and classify three categories of cry, namely ‘pain’, ‘fear’ and ‘hunger’ and the results demonstrated that the highest classification rate was achieved by using feed-forward neural network. Another research work carried out by (Cano & Escobedo, 1999) used the Kohonen's self-organizing maps (SOM) which is basically a variety of unsupervised ANN technique to classify different infant cries. (Rosales-Pérez, Reyes-Garcia, Gonzalez, & Arch-Tirado, 2012) used Genetic Selection of a Fuzzy Model (GSFM) for classification of infant cry where GSFM selects a combination of feature selection methods, type of fuzzy processing, learning algorithm, and its associated parameters that best fit to the data and have obtained up to 99.42% in recognition accuracy. (Al-Azzawi, 2014) designed an automatic infant cry recognition system based on the fuzzy transform (F-transform) that classifies two different kinds of cries, which come from physiological status and medical disease, a supervised MLP scaled conjugate ANN was used and the classification accuracy obtained was 96%.

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