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A Hybrid System for Automatic Infant Cry Recognition II
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Author(s): Carlos Alberto Reyes-García (Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico), Sandra E. Barajas (Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico), Esteban Tlelo-Cuautle (Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico)and Orion Fausto Reyes-Galaviz (Universidad Autónoma de Tlaxcala, Mexico)
Copyright: 2009
Pages: 6
Source title:
Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch128
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Abstract
Automatic Infant Cry Recognition (AICR) process is basically a problem of pattern processing, very similar to the Automatic Speech Recognition (ASR) process (Huang, Acero, Hon, 2001). In AICR first we perform acoustical analysis, where the crying signal is analyzed to extract the more important acoustical features, like; LPC, MFCC, etc. (Cano, Escobedo and Coello, 1999). The obtained characteristics are represented by feature vectors, and each vector represents a pattern. These patterns are then classified in their corresponding pathology (Ekkel, 2002). In the reported case we are automatically classifying cries from normal, deaf and asphyxiating infants. We use a genetic algorithm to find several optimal parameters needed by the Fuzzy Relational Neural Network FRNN (Reyes, 1994), like; the number of linguistic properties, the type of membership function, the method to calculate the output and the learning rate. The whole model has been tested on several data sets for infant cry classification. The process, as well as some results, is described.
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