Fundamentals of speech recognition
Fundamentals of speech recognition
Forecasting exchange rates using general regression neural networks
Computers and Operations Research - Neural networks in business
Hand geometry identification without feature extraction by general regression neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Advances in Engineering Software
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A combined classifier of cry units with new acoustic attributes
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Mathematical and Computer Modelling: An International Journal
Pathological infant cry analysis using wavelet packet transform and probabilistic neural network
Expert Systems with Applications: An International Journal
A novel objective function for improved phoneme recognition using time-delay neural networks
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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A cry is the first verbal communication of infants and it is described as a loud, high-pitched sound made by infants in response to certain situations. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and it has been proven to be an excellent tool to investigate the pathological status of an infant. This paper proposes short-time Fourier transform (STFT) based time-frequency analysis of infant cry signals. Few statistical features are derived from the time-frequency plot of infant cry signals and used as features to quantify infant cry signals. Two types of radial basis neural networks such as Probabilistic Neural Network (PNN) and General Regression Neural Network are employed as classifiers for discriminating infant cry signals. Two classes of infant cry signals are considered such as normal cry signals and pathological cry signals of infants with asphyxia. For comparison, the proposed features are also tested using two neural network models such as Multilayer Perceptron (MLP) and Time-Delay Neural Network (TDNN) trained by scaled conjugate gradient algorithm. The experimental results show that the PNN and GRNN give very promising classification accuracy compared to MLP and TDNN and the proposed methods can effectively classify normal and pathological infant cries of infants with asphyxia.