基于支持向量机的咳嗽自动识别

doi:10.3969/j.issn.1006-2475.2016.07.023

计算机与现代化 ›› 2016, Vol. 251 ›› Issue (07): 111-114.doi: 10.3969/j.issn.1006-2475.2016.07.023

基于支持向量机的咳嗽自动识别

1.电子科技大学中山学院机电工程学院,广东中山528403；
2.华南理工大学自动化科学与工程学院，广东广州510641

收稿日期:2016-03-15 出版日期:2016-07-21 发布日期:2016-07-22
作者简介: 朱春媚(1981-)，女，广东河源人，电子科技大学中山学院机电工程学院讲师，华南理工大学自动化科学与工程学院博士研究生，研究方向：信号处理；黎萍(1981-)，女，江西萍乡人，副教授，博士，研究方向：人工智能。
基金资助:
中山市科技计划项目(2014A2FC383)

Automatic Recognition of Cough Based on Support Vector Machine

1.Mechanical and Electrical Engineering College, Zhongshan Institute, University of Electronic
Science and Technology, Zhongshan 528403, China；
2.College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China

Received:2016-03-15 Online:2016-07-21 Published:2016-07-22

摘要/Abstract

摘要： 为了进一步改善咳嗽自动识别的效果，本文以支持向量机作为咳嗽识别的分类模型，详细介绍样本采集、MFCC特征参数提取和支持向量机咳嗽识别的实现过程，并与隐马尔可夫模型和动态时间规划的识别结果及运行时间进行比较。实验结果表明在识别率方面，当训练样本集较大时，支持向量机与隐马尔可夫模型的识别结果相近且优于动态时间规划；当训练样本集较小时，支持向量机的识别率最高。在训练和识别效率方面，支持向量机具有明显的优势。

关键词: , 咳嗽识别, 支持向量机, 特征提取, 隐马尔可夫模型

Abstract: To further improve the effect of cough automatic identification, support vector machine is adopted as classification model for cough recognition. The process of sample collection, MFCC feature extraction and support vector machine cough recognition is introduced in detail, and the results are compared with hidden Markov model and dynamic time warping. Experiment results show that, with a big training sample set, recognition rates of support vector machine are similar with hidden Markov model and higher than dynamic time warping, while with a small training sample set, support vector machine achieves the best result. In terms of efficiency of the algorithm, support vector machine significantly outperforms the other two classification models in both training and recognition time.

Key words: cough recognition, support vector machine, feature extraction, hidden Markov model

朱春媚1，2，黎萍1. 基于支持向量机的咳嗽自动识别[J]. 计算机与现代化, 2016, 251(07): 111-114.

ZHU Chun-mei1,2, LI Ping1. Automatic Recognition of Cough Based on Support Vector Machine[J]. Computer and Modernization, 2016, 251(07): 111-114.

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基于支持向量机的咳嗽自动识别

Automatic Recognition of Cough Based on Support Vector Machine

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