Investigation of Mel Frequency Cepstrum Coefficients parameters for classification of infant cries with hypothyroidism using MLP classifier

Abstract

Hypothyroidism in infants is caused by insufficient production of hormones by the thyroid gland. Due to stress in the chest cavity due to the enlarged liver, the cry signals are unique and can be distinguished from healthy infant cries. We investigate the usage of the Multilayer Perceptron (MLP) classifier to diagnose infant hypothyroidism. The Mel Frequency Cepstrum Coefficients (MFCC) feature extraction method was used to extract important information from the cry signal itself. This study investigates the number of filter banks and coefficients in MFCC to extract optimal information from infant cry signals, to be classified using MLP. The cry signals were first divided into equal-length segments, and MFCC was used to extract features from them. Tests on the combined University of Milano-Bicocca and Instituto Nacional de Astrofisica datasets yielded MLP classification accuracy of 89.18%, suggesting that the optimal MFCC resolution was obtained using 36 filter banks, and 19 coefficients.