gammatone短时谱自相似度的病理嗓音检测  

Pathological voice detection based on gammatone short time spectral self-similarity

作　　者：赵登煌 周长伟 朱欣程 张晓俊 陶智 ZHAO Denghuang;ZHOU Changwei;ZHU Xincheng;ZHANG Xiaojun;TAO Zhi(School of Optoelectronic Science and Engineering,Soochow University,Suzhou,Jiangsu 215000,P.R.China)

机构地区：[1]苏州大学光电科学与工程学院,江苏苏州215000

出　　处：《生物医学工程学杂志》2022年第4期694-701,712,共9页Journal of Biomedical Engineering

基　　金：国家自然科学基金项目(61271359);教育部光电教指分委教育教学研究项目(gdyljs52);苏州大学高等教育教改研究课题(5731503920)。

摘　　要：基于机器学习和信号处理的声学检测方法是目前病理嗓音检测的重要手段,嗓音特征的提取是其中至关重要的一环。目前广泛使用的特征,存在依赖基频提取、易受噪声影响、计算复杂度高等不足。针对这些不足,本文提出了一种基于多频带分析和混沌分析的病理嗓音检测方法。使用gammatone滤波器组模拟人耳听觉特性进行多频带分析,获取不同频带的信号;根据嗓音中的混沌现象带来的湍流噪声会恶化频谱收敛性的特点,对每个频带的信号进行短时傅里叶变换,提取特征gammatone短时谱自相似度(GSTS),分析每个频带信号的混沌程度,来区分正常和病理嗓音。实验结果显示,结合传统机器学习方法,GSTS在马萨诸塞州眼耳医院(MEEI)病理嗓音数据库中识别准确率达到99.50%,相比已有识别率最高的特征提升3.46%,同时特征提取时间相比传统非线性特征大幅减少。该结果表明,相比已有特征,GSTS具有更高的提取效率和更好的识别效果。The acoustic detection method based on machine learning and signal processing is an important method of pathological voice detection and the extraction of voice features is one of the most important.Currently,the features widely used have disadvantage of dependence on the fundamental frequency extraction,being easily affected by noise and high computational complexity.In view of these shortcomings,a new method of pathological voice detection based on multi-band analysis and chaotic analysis is proposed.The gammatone filter bank was used to simulate the human ear auditory characteristics to analyze different frequency bands and obtain the signals in different frequency bands.According to the characteristics that turbulence noise caused by chaos in voice will worsen the spectrum convergence,we applied short time Fourier transform to each frequency band of the voice signal,then the feature gammatone short time spectral self-similarity(GSTS)was extracted,and the chaos degree of each band signal was analyzed to distinguish normal and pathological voice.The experimental results showed that combined with traditional machine learning methods,GSTS reached the accuracy of 99.50%in the pathological voice database of Massachusetts Eye and Ear Infirmary(MEEI)and had an improvement of 3.46%compared with the best existing features.Also,the time of the extraction of GSTS was far less than that of traditional nonlinear features.These results show that GSTS has higher extraction efficiency and better recognition effect than the existing features.

关 键 词：病理嗓音识别 混沌 gammatone滤波器组 短时谱自相似度 

分 类 号：TN912.3[电子电信—通信与信息系统] R767.92[电子电信—信息与通信工程]