Respiratory Sounds Classification Base On Gaussian Mixture Models
Date Issued
2006
Date
2006
Author(s)
Liu, Bor-Chin
DOI
zh-TW
Abstract
Traditional wheezes detection method are based on searching for the frequencies and durations of wheezes or the peaks from successive spectra.In these methods,the discriminative threshold used to identify peaks is fixed empirically.The objective of this study is to classify normal and abnormal (wheezing) respiratory sounds using the Cepstral analysis(Mel Frequency Cepstral Coefficients ,MFCC) is proposed with Gaussian Mixture Models (GMM) method. Gaussian Mixture Models(GMM) is a powerful statistical method massively used for speaker identification. In the respiratory sound that is obtained by training. During the test phase, an unknown sound is compared to all the GMM on the models and the classification decision is based on the Maximum Likelihood (ML) criterion.
Subjects
梅爾刻度式倒頻譜參數
向量量化
期望值最大演算法
最大概似值法
wheezes
respiratory sounds
cepstral analysis
Mel Frequency Cepstral Coefficients
Gaussian Mixture Models
Maximum Likelihood
Type
thesis
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