摘要
快速准确地检测出采集录音中的咳嗽部分对许多呼吸道疾病的临床诊断有着重要意义。使用梅尔频率倒谱系数(MFCC)作为特征参数来分析所要处理的声音信号,并用多组训练数据分别为采集录音中的咳嗽音、说话声、笑声、清喉音等数据各建立两个高斯混合模型(GMM),将每类数据得到的两个GMM进行线性组合得到最终的表示每类数据的概率模型,进而实现对咳嗽音部分的检测。在此基础上引入了小波去噪理论,分别对每段数据去噪并进行端点检测。仿真实验结果表明所提方法能够有效提高系统的识别性能。
Rapid and precise detection of cough sound from continuous recordings is meaningful for clinical diagnosis of many respiratory diseases.This paper uses Mel-frequency cepstral coefficient as the classification feature to analyze the sound signal to be processed and creates two corresponding Gaussian mixture models for the cough sound, speech voice,laughter and throat clearing sound in the recordings respectively using multiple groups of training data,then the ultimate probability models are acquired through the means of linear combination of the two GMMs of each class.Furthermore, the theory of wavelet denoising is introduced to denoise each sound signal and then detect its endpoints.Simulation experimental results indicate that the proposed method can effectively improve the performance of the detection.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第32期151-154,共4页
Computer Engineering and Applications
关键词
咳嗽音检测
梅尔频率倒谱系数
高斯混合模型
线性组合
小波去噪
cough sound detection
Mel-frequency cepslral coefficient
Gaussian mixture model
linear combination
wavelet denoise
