中文摘要：

咳嗽是呼吸道疾病中一种常见的症状,基于模式识别算法可以对语音信号中咳嗽对象的频度和强度进行客观化分析,进而帮助临床咳嗽的诊断及病程跟踪.在临床录制的连续语音信号中检测出咳嗽对象是咳嗽诊断及分析的基础.本文将咳嗽检测视为模式识别中的二分类问题,借助于分类器将咳嗽对象从背景信号中分离.在深入研究咳嗽频谱分布的基础上,提出一种新的基于高频子带的特征提取方法（High-frequency subband features method）,在提取咳嗽信号特征之前,使用高频滤波器获取高频部分信号.在合成实验数据的过程中使用了不同的噪声类型和信噪比来组成不同的实验环境,并且在每种实验环境下对几种特征提取方法进行了评价与分析.实验结果表明,相比于常见的语音信号特征,结合基于高频子带特征的咳嗽检测方法在检测正确率等性能指标上有显著地提升.

英文摘要：

Cough is a very common symptom in respiratory diseases.Objective analysis on the frequency and intensity of cough by pattern recognition algorithm can provide more valuable clinical information for patients with chronic cough and help them with cough tracking and diagnosis.Cough detection is the basis of the diagnosis and analysis of cough in clinical continuous recordings.In this article,we consider cough detection problem as a binary classification ones and make use of classifier to segregate cough from background noise for the purpose of cough detection.We propose a novel high-frequency subband features method on the basis of in-depth study of the spectral distribution of cough.It is found that the energy of cough signal is distributed widely and concentrated in the high frequency region,which is very different from spectral patterns of speech signals.So in experiments,we firstly extract subband features of which frequency region varies from low frequency to high frequency using filter banks,and then find the performance of high frequency-subband features which is superior to that of low frequency-subband.Finally,the high-fre-quency subband method uses high-frequency filter to get corresponding high frequency signal before extracting the features of cough.The method synthesizes the experimental data under the condition of different noisy type and SNR（signal to noise ratio）,then compares and analyses the performance of different feature extraction methods under specific noisy conditions.Experimental results demonstrate that compared with traditional audio feature extraction method,the method based on high-frequency subband features achieves substantial performance improvement in recognition.