Respiratory sounds classification using cepstral analysis and Gaussian mixture models

The cepstral analysis is proposed with Gaussian mixture models (GMM) method to classify respiratory sounds in two categories: normal and wheezing. The sound signal is divided in overlapped segments, which are characterized by a reduced dimension feature vectors using Mel-frequency cepstral coefficients (MFCC) or subband based cepstral parameters (SBC). The proposed schema is compared with other classifiers: vector quantization (VQ) and multi-layer perceptron (MLP) neural networks. A post processing is proposed to improve the classification results.