Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
DARPA Neural Network Stdy
Fuzzy Relational Compression Applied on Feature Vectors for Infant Cry Recognition
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
A cry-based babies identification system
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Modelling and Simulation in Engineering
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This work presents an infant cry automatic recognizer development, with the objective of classifying three kinds of infant cries, normal, deaf and asphyxia from recently born babies. We use extraction of acoustic features such as LPC (Linear Predictive Coefficients) and MFCC (Mel Frequency Cepstral Coefficients) for the cry's sound waves, and a genetic feature selection system combined with a feed forward input delay neural network, trained by adaptive learning rate back-propagation. We show a comparison between Principal Component Analysis and the proposed genetic feature selection system, to reduce the feature vectors. In this paper we describe the whole process; in which we include the acoustic features extraction, the hybrid system design, implementation, training and testing. We also show the results from some experiments, in which we improve the infant cry recognition up to 96.79% using our genetic system. We also show different features extractions that result on vectors that go from 145 up to 928 features, from cry segments of 1 and 3 seconds respectively.