基于主元分析法和非均匀滤波器组的咳嗽信号特征提取  

Feature Extraction for Cough-sound Recognition Based on Principle Component Analysis and Non-uniform Filter-bank

作　　者：朱春媚[1,2] 莫鸿强[2] 田联房[2] 郑则广[3] 

机构地区：[1]电子科技大学中山学院机电工程学院,中山528403 [2]华南理工大学自动化科学与工程学院,广州510641 [3]广州医学院第一附属医院,广州510120

出　　处：《生物医学工程学杂志》2015年第4期746-750,共5页Journal of Biomedical Engineering

基　　金：华南理工大学中央高校基本科研基金资助项目(2012ZZ0106);中山市科技计划项目资助(2014A2FC383)

摘　　要：咳嗽识别在临床上具有重要的诊断指导意义。针对咳嗽频谱能量的分布特点,本文提出了一种新的梅尔(Mel)频率倒谱系数(MFCC)提取方法。将咳嗽频谱划分为若干个频段,采用主元分析方法计算各频段的能量强度系数,根据强度系数的插值曲线分配滤波器个数,设计Mel刻度上非均匀分布的滤波器组进行MFCC特征提取。基于隐马尔可夫模型(HMM)的咳嗽识别实验表明,该方法可以有效改善咳嗽识别的效果。Cough recognition provides important clinical information for the treatment of many respiratory diseases. A new Mel frequency cepstrum coefficient （MFCC） extracting method has been proposed on the basis of the distributional characteristics of cough spectrum. The whole frequency band was divided into several sub-bands, and the ener gy coefficient for each band was obtained by method of principle component analysis. Then non-uniform filter-bank in Mel frequency is designed to improve the extracting process of MFCC by distributing filters according to the spectrum energy coefficients. Cough recognition experiment using hidden Markov model was carried out, and the results showed that the proposed method could effectively improve the performance of cough recognition.

关 键 词：咳嗽 特征提取 主元分析 滤波器组 

分 类 号：R56[医药卫生—呼吸系统] TN911.7[医药卫生—内科学]