Abstract
In this paper we present the experiments and results obtained in the classification of infant cry using a variety of classifiers, ensembles among them. Three kinds of cry were classified: normal (without detected pathology), hypo acoustic (deaf), and asphyxia. The feature vectors were formed by the extraction of Mel Frequency Cepstral Coefficients (MFCC); these were then processed and reduced through the application of five statistics operations, namely: minimum, maximum, average, standard deviation and variance. For the classification there were used supervised machine learning methods as Support Vector Machines, Neural Networks, J48, Random Forest and Naive Bayes. The ensembles used were combinations of these under different approaches like Majority Vote, Staking, Bagging and Boosting. The 10-fold cross validationtechnique was used to evaluate precision in all classifiers.
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References
Wasz-Hockert, O., Lind, J., Vuorenkoski, V., Partanen, T., Valanne, E.: El Llanto en el Lactante y su Significación Diagnóstica, Editorial Científico Médica, España (1970)
WEKA: Waikato Environment for Knowledge Analysis, University of Waikato New Zealand (1999-2007)
Orozco, J.: Extracción y Análisis de Características Acústicas del Llanto de Bebés para su Reconocimiento Automático Basado en Redes Neuronales, Master Thesis, Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico (2003)
Barajas, S.E.: Clasificación de Llanto Infantil, Master Thesis, Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico (2006)
Platt, J.C.: Fast Training of Support Vector Machines using Sequential Minimal Optimization, Microsoft Research, EUA (2000)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA (1993)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36, 105–142 (1999)
Wolpert, H., Stacked, D.: Generalization. Neural Networks 5, 241–259 (1992)
Cano, S.D., Escobedo, D.I., Regueiferos, L., Capdevila, L.: 15 Años del Cry Analysis en Cuba: Resultados y Perspectivas, VI Congreso Internacional de Informática en Salud, Santiago de Cuba (2007)
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Amaro-Camargo, E., Reyes-García, C.A. (2007). Applying Statistical Vectors of Acoustic Characteristics for the Automatic Classification of Infant Cry. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_109
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DOI: https://doi.org/10.1007/978-3-540-74171-8_109
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