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
MICAI '08 Proceedings of the 2008 Seventh Mexican International Conference on Artificial Intelligence
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
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 comparative study of wavelet families for classification of wrist motions
Computers and Electrical Engineering
Artificial Intelligence in Medicine
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
Crying is the most noticeable behavior of infancy. 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. General Regression Neural Network (GRNN) is employed as a classifier for discriminating infant cry signals. Two classes of infant cry signals are considered such as normal cry signals and pathological cry signals from deaf infants. To prove the reliability of the proposed features, two neural network models such as Multilayer Perceptron (MLP) and Time-Delay Neural Network (TDNN) trained by scaled conjugate gradient algorithm are also used as classifiers. The experimental results show that the GRNN classifier gives very promising classification accuracy compared to MLP and TDNN and the proposed method can effectively classify normal and pathological infant cries.