Spectro-temporal analysis of HIE and asthma infant cries using auditory spectrogram

Abstract

In this paper, auditory spectrogram is proposed for analysis of HIE and asthma infant cries. Auditory spectrogram represents a 2-dimensional (i.e., 2-D) pattern of neural activity, distributed along a logarithmic frequency-axis. Features are derived from the auditory spectrograms of each class. These features are then used to train support vector machine (SVM) classifier. Effectiveness of the proposed features is shown by application of proposed features for classification of pathologies. Classification accuracy achieved with SVM classifier with radial basis function (RBF) kernel is 87.67%. Classification performance has been compared with the state-of-the-art method, i.e., Mel Frequency Cepstral Coefficients (MFCC). It has been observed that MFCC features are giving 86.13% classification accuracy. Fusion of proposed features with the MFCC features further improves the classification accuracy to 88.54%. High classification accuracy of auditory spectrogram can be attributed to its ability to retain both formant frequencies and low frequency harmonics.