Classification of pathological infant cries using modulation spectrogram features

In this paper, feature derived from modulation spectrogram is proposed for the classification of pathological infant cries. In our work, two pathologies are considered, viz., asthma and hypoxy ischemic encephalopathy (HIE). Modulation spectrogram features are arranged in a form of tensor which is then reduced in its dimensions using Higher Order Singular Value Decomposition Theorem (HOSVD). The reduced feature set is used for classification of pathological infant cries using support vector machine (SVM) classifier with radial basis function (RBF) kernel. The classifier gives a mean classification accuracy of 76.23 % with the proposed feature set. The same experimental setup is used for the conventional feature set, i.e., Mel frequency cepstral coefficients (MFCC). MFCC shows a classification accuracy of 64.43 %. It is also observed that the proposed approach is robust against signal degradation conditions.