Applying statistical vectors of acoustic characteristics for the automatic classification of infant cry

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 (deal), 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 validation technique was used to evaluate precision in all classifiers.