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用于哮鸣音分析的梅尔频率倒谱评估

Evaluation of Mel-Frequency Cepstrum for Wheeze Analysis.

作者信息

Pramono Renard Xaviero Adhi, Imtiaz Syed Anas, Rodriguez-Villegas Esther

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:4686-4689. doi: 10.1109/EMBC.2019.8857848.

Abstract

Monitoring of wheezes is an integral part of managing Chronic Respiratory Diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD). Recently, there is a growing interest in automatic detection of wheezes and the use of Mel-Frequency Cepstral Coefficients (MFCC) have been shown to achieve encouraging detection performance. While the successful use of MFCC for identifying wheezes has been demonstrated, it is not clear which MFCC coefficients are actually useful for detecting wheezes. The objective of this paper is to characterize and study the effectiveness of individual coefficients in discriminating between wheezes and normal respiratory sounds. The coefficients have been evaluated in terms of histogram dissimilarity and linear separability. Further, a comparison between the use of single coefficient against other combinations of coefficients is also presented. The results demonstrate MFCC-2 coefficient to be significantly more effective than all the other coefficients in discriminating between wheezes and normal respiratory sounds sampled at 8000 Hz.

摘要

对哮鸣音的监测是管理慢性呼吸道疾病(如哮喘和慢性阻塞性肺疾病(COPD))不可或缺的一部分。最近,人们对哮鸣音的自动检测越来越感兴趣,并且已经证明使用梅尔频率倒谱系数(MFCC)可实现令人鼓舞的检测性能。虽然已经证明MFCC在识别哮鸣音方面的成功应用,但尚不清楚哪些MFCC系数实际上对检测哮鸣音有用。本文的目的是表征和研究各个系数在区分哮鸣音和正常呼吸音方面的有效性。已根据直方图差异和线性可分性对这些系数进行了评估。此外,还比较了使用单个系数与其他系数组合的情况。结果表明,在区分8000Hz采样的哮鸣音和正常呼吸音方面,MFCC-2系数比所有其他系数都要有效得多。

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