【摘要】：咳嗽的自動分類在臨床上具有重要的輔助診斷作用。傳統(tǒng)的Mel頻率倒譜系數(shù)(MFCC)采用Mel均勻?yàn)V波器組,高頻段的濾波器分布較稀疏,未能最大程度反映兩類咳嗽的特征差別。針對這個(gè)問題,本文在分析干性咳嗽和濕性咳嗽頻譜能量分布特點(diǎn)的基礎(chǔ)上,提出了一種改進(jìn)的反向MFCC提取方法,采用反向Mel刻度上的均勻?yàn)V波器組,并放置在兩類咳嗽都具有高頻譜能量的頻段,使得特征提取集中在兩類咳嗽特征信息豐富且差別顯著的頻段進(jìn)行�；陔[馬爾可夫模型的咳嗽干濕性自動分類實(shí)驗(yàn)結(jié)果表明,該方法獲得了優(yōu)于傳統(tǒng)MFCC的分類性能,總體分類準(zhǔn)確率從89.76%提高到了93.66%。
[Abstract]:Automatic classification of cough plays an important role in clinical diagnosis. The traditional Mel frequency cepstrum coefficient (MFCC) uses Mel uniform filter banks, and the filter distribution in high frequency band is sparse, which can not reflect the characteristic difference of two kinds of cough to the greatest extent. In order to solve this problem, based on the analysis of spectrum energy distribution characteristics of dry cough and wet cough, an improved reverse MFCC extraction method is proposed, which uses a uniform filter bank based on reverse Mel scale. And placed in the two kinds of cough have high frequency spectrum energy, so the feature extraction is concentrated in the two kinds of cough feature information rich and significant differences in frequency bands. The experimental results of automatic classification of cough dryness and wetness based on hidden Markov model show that this method has better classification performance than traditional MFCC, and the overall classification accuracy is improved from 89.76% to 93.66%.
【作者單位】： 電子科技大學(xué)中山學(xué)院機(jī)電工程學(xué)院;華南理工大學(xué)自動化科學(xué)與工程學(xué)院;廣州醫(yī)學(xué)院第一附屬醫(yī)院;
【基金】：中央高�；究蒲袑ｍ�(xiàng)基金項(xiàng)目資助(2012ZZ0106) 中山市科技計(jì)劃項(xiàng)目資助(2014A2FC383)
【分類號】：R56;TP391.7
，

本文編號：2136075