Optimization of cepstral features for robust lung sound classification

Detection of lung abnormalities by characterizing lung sounds has been a primary step for clinical examination for a pulmonologist. This work focuses on utilization of cepstral features for lung sound analysis and classification. The proposed method incorporates statistical properties of cepstral features along with artificial neural network (ANN) based classification. Experimental results indicate that the proposed features outperform the wavelet-based features and conventional mel-frequency cepstral features. Further analyses have been performed on the proposed features to experimentally optimize the frame size and feature dimensionality. We also look at optimizing number of hidden layer nodes to improve robustness. We have found that the optimized features perform better for a wide range of signal-to-noise ratio (SNR) values.