Neural Networks
Fundamentals of speech recognition
Fundamentals of speech recognition
MICAI '08 Proceedings of the 2008 Seventh Mexican International Conference on Artificial Intelligence
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Analysis of an infant cry recognizer for the early identification of pathologies
Nonlinear Speech Modeling and Applications
Classification of Speech Dysfluencies Using LPC Based Parameterization Techniques
Journal of Medical Systems
A comparative study of wavelet families for classification of wrist motions
Computers and Electrical Engineering
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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Acoustic analysis of infant cry signals has been proven to be an excellent tool in the area of automatic detection of pathological status of an infant. This paper investigates the application of parameter weighting for linear prediction cepstral coefficients (LPCCs) to provide the robust representation of infant cry signals. Three classes of infant cry signals were considered such as normal cry signals, cry signals from deaf babies and babies with asphyxia. A Probabilistic Neural Network (PNN) is suggested to classify the infant cry signals into normal and pathological cries. PNN is trained with different spread factor or smoothing parameter to obtain better classification accuracy. The experimental results demonstrate that the suggested features and classification algorithms give very promising classification accuracy of above 98% and it expounds that the suggested method can be used to help medical professionals for diagnosing pathological status of an infant from cry signals.