计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (10): 2345-2356.DOI: 10.3778/j.issn.1673-9418.2102055

• 人工智能 • 上一篇    下一篇

帕金森语音障碍的Mel变换域局部梯度统计分析

张涛1,2,+(), 林丽琴1,2, 张亚娟1,2, 牛晓霞1,2   

  1. 1.燕山大学 信息科学与工程学院,河北 秦皇岛 066004
    2.燕山大学 河北省信息传输与信号处理重点实验室,河北 秦皇岛 066004
  • 收稿日期:2021-02-24 修回日期:2021-05-20 出版日期:2022-10-01 发布日期:2021-05-31
  • 通讯作者: + E-mail: zhtao@ysu.edu.cn
  • 作者简介:张涛(1979—),男,河北秦皇岛人,博士,副教授,CCF会员,主要研究方向为医学信息处理、机器学习、概念认知学习等。
    林丽琴(1997—),女,山东烟台人,硕士研究生,主要研究方向为语音信号分析、机器学习等。
    张亚娟(1993—),女,河北保定人,硕士研究生,主要研究方向为医学信息处理、机器学习等。
    牛晓霞(1981—),女,河北保定人,博士,高级实验师,主要研究方向为智能信号处理、机器学习等。
  • 基金资助:
    国家自然科学基金(61971374);河北省自然科学基金(F2020203010)

Statistical Analysis of Local Gradient in Mel Transform Domain for Parkinson’s Dysphonia

ZHANG Tao1,2,+(), LIN Liqin1,2, ZHANG Yajuan1,2, NIU Xiaoxia1,2   

  1. 1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
    2. Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, Hebei 066004, China
  • Received:2021-02-24 Revised:2021-05-20 Online:2022-10-01 Published:2021-05-31
  • About author:ZHANG Tao, born in 1979, Ph.D., associate professor, member of CCF. His research interests include medical signal processing, machine learning, concept-cognitive learning, etc.
    LIN Liqin, born in 1997, M.S. candidate. Her research interests include speech signal analysis, machine learning, etc.
    ZHANG Yajuan, born in 1993, M.S. candidate. Her research interests include medical signal processing, machine learning, etc.
    NIU Xiaoxia, born in 1981, Ph.D., senior experi-mentalist. Her research interests include intelligent signal processing, machine learning, etc.
  • Supported by:
    National Natural Science Foundation of China(61971374);Natural Science Foundation of Hebei Province(F2020203010)

摘要:

帕金森病语音障碍分析是进行基于语音的帕金森病早期诊断的信息分析基础。近年来,随着研究的深入,Mel变换域信息在本领域表现出越来越多的优势,同时提取结构特征对分类性能的提升日益显现。从帕金森病人语音信号的Mel变换域信息结构出发,提出Mel变换域局部梯度统计特征提取方法。该方法首先通过Mel频率变换的方法将语音信号转化为时频变换域能量信号,并将能量谱进行可视化表示;其次对能量数据进行滑动窗口处理,计算检测窗口内每个能量点的梯度与角度,获得Mel变换域的局部结构信息;最后根据角度统计所有检测窗口能量点的梯度,从而得到整体的局部梯度统计特征,以此表示Mel变换域中能量值的变化情况。在不同的帕金森病语音数据集上利用不同分类器进行实验,实验结果表明,与Mel变换域分析、倒谱分析和深度学习等方法相比,所提算法具有高准确度、高灵敏性的特点,从而验证了提出的局部梯度统计特征在帕金森语音障碍分析中的有效性。

关键词: 帕金森病, 语音障碍, Mel变换域, 局部梯度统计

Abstract:

Dysphonia analysis of Parkinson’s disease is the basis of information analysis for early diagnosis of Parkinson’s disease based on speech. In recent years, with the deepening of research, Mel transform domain information shows more and more advantages in this field. At the same time, the improvement of classification performance by extracting structural features is increasingly apparent. This paper proposes a method for local gradient statistical feature extraction in Mel transform domain from the point of the structure of Mel transform domain information of speech signals of people with Parkinson’s disease. Firstly, the speech signal is converted into the energy signal in the time-frequency transform domain by the method of Mel frequency transformation, and the energy spectrum is represented visually. Then, the energy data are processed by sliding window, and the local structure information of the Mel transform domain is obtained by calculating the gradient and angle of each energy point in the detection window. Finally, the gradients of the energy points of all detection windows are calculated according to the angles to obtain the local gradient statistical features, which represent the change of energy value in Mel transform domain. The results of the experiments performed on different datasets by different classifiers show that compared with the methods of Mel transform domain analysis, cepstrum analysis and deep learning, the local gradient statistical features in Mel transform domain are superior to them in classification accuracy and sensitivity, thereby verifying the effectiveness of the local gradient statistical feature in the dysphonia analysis of Parkinson’s disease.

Key words: Parkinson’s disease, dysphonia, Mel transform domain, local gradient statistics

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