title: Respiratory sounds classification using cepstral analysis and Gaussian mixture models.
abstract: 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.
authors.affiliation: Département de Mathématiques, d'Informatique et de Génie, Université du Québec à Rimouski, Que., Canada.
authors.forename: M
authors.initials: M
authors.lastname: Bahoura
authors.forename: C
authors.initials: C
authors.lastname: Pelletier