Zhu Chunmei, Liu Baojun, Li Ping, Mo Hongqiang, Zheng Zeguang
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2016 Apr;33(2):239-43.
Automatic classification of different types of cough plays an important role in clinical.In the previous research of cough classification or cough recognition,traditional Mel frequency cepstrum coefficients(MFCC)which extracts feature mainly from low frequency band is usually used as feature expression.In this paper,by analyzing the distributions of spectral energy of dry/wet cough,it is found that spectral difference of two types of cough exits mainly in middle frequency band and high frequency band.To better reflect the spectral difference of dry cough and wet cough,an improved method of extracting reverse MFCC is proposed.In this method,reverse Mel filter-bank in which filters are allocated in reverse Mel scale is adopted and is improved by placing filters only in the frequency band with high spectral energy.As a result,features are mainly extracted from the frequency band where two types of cough show both high spectral energy and distinguished difference.Detailed process of accessing improved reverse MFCC was introduced and hidden Markov models trained by 60 dry cough and 60 wet cough were used as cough classification model.Classification experiment results for 120 dry cough and 85 wet cough showed that,compared to traditional MFCC,better classification performance was achieved by the proposed method and the total classification accuracy was raised from 89.76%to 93.66%.
不同类型咳嗽的自动分类在临床上具有重要作用。在以往的咳嗽分类或咳嗽识别研究中，通常使用主要从低频段提取特征的传统梅尔频率倒谱系数（MFCC）作为特征表示。本文通过分析干咳/湿咳的频谱能量分布，发现两种类型咳嗽的频谱差异主要存在于中频段和高频段。为了更好地反映干咳和湿咳的频谱差异，提出了一种改进的反向MFCC提取方法。在该方法中，采用了滤波器按反向梅尔尺度分配的反向梅尔滤波器组，并通过仅在频谱能量高的频段放置滤波器进行了改进。结果，特征主要从两种类型咳嗽均具有高频谱能量且差异明显的频段提取。介绍了获取改进反向MFCC的详细过程，并将由60例干咳和60例湿咳训练的隐马尔可夫模型用作咳嗽分类模型。对120例干咳和85例湿咳的分类实验结果表明，与传统MFCC相比，该方法取得了更好的分类性能，总分类准确率从89.76%提高到了93.66%。
