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- W1844397772 abstract "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." @default.
- W1844397772 created "2016-06-24" @default.
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- W1844397772 date "2005-01-01" @default.
- W1844397772 modified "2023-09-24" @default.
- W1844397772 title "Infant Cry Classification to Identify Hypo Acoustics and Asphyxia Comparing an Evolutionary-Neural System with a Neural Network System" @default.
- W1844397772 cites W2054816540 @default.
- W1844397772 doi "https://doi.org/10.1007/11579427_97" @default.
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