基于声音空间梯度的高稳健性击键识别方法

刘影; 韩康康; 钱志鸿

doi:10.11959/j.issn.1000-436x.2020095

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基于声音空间梯度的高稳健性击键识别方法

学术论文 | 更新时间：2024-06-05

- 基于声音空间梯度的高稳健性击键识别方法
- High-roubustness keystroke recognition method based on acoustic spatial gradient
- 通信学报 2020年41卷第5期页码：96-103
- 作者机构：
  
  1. 辽宁工程技术大学电子与信息工程学院，辽宁葫芦岛 125105
  2. 北京邮电大学电子工程学院，北京 100876
  3. 吉林大学通信工程学院，吉林长春 130022
- 作者简介：
  
  [ "刘影（1983- ），女，吉林大安人，博士，辽宁工程技术大学副教授，主要研究方向为无线定位、智能无线感知" ]
  [ "韩康康（1997- ），男，河南商丘人，北京邮电大学硕士生，主要研究方向为认知无线电、智能信息处理" ]
  [ "钱志鸿（1957- ），男，吉林长春人，博士，吉林大学教授、博士生导师，主要研究方向为基于物联网、D2D、Wi-Fi、RFID等的无线网络与通信技术" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB1403300);辽宁省教育厅一般项目资金资助项目(LJ2017QL013);辽宁省博士启动基金资助项目(20170520098);辽宁省自然科学基金资助项目(2019-ZD-0038);辽宁省创新人才支持计划基金资助项目(LR2016045)
- DOI：10.11959/j.issn.1000-436x.2020095
  中图分类号： TP391
- 网络首发：2020-05，
  
  纸质出版：2020-05-25
- 稿件说明：
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刘影, 韩康康, 钱志鸿. 基于声音空间梯度的高稳健性击键识别方法[J]. 通信学报, 2020,41(5):96-103.

Ying LIU, Kangkang HAN, Zhihong QIAN. High-roubustness keystroke recognition method based on acoustic spatial gradient[J]. Journal on Communications, 2020, 41(5): 96-103.
刘影, 韩康康, 钱志鸿. 基于声音空间梯度的高稳健性击键识别方法[J]. 通信学报, 2020,41(5):96-103. DOI： 10.11959/j.issn.1000-436x.2020095.

Ying LIU, Kangkang HAN, Zhihong QIAN. High-roubustness keystroke recognition method based on acoustic spatial gradient[J]. Journal on Communications, 2020, 41(5): 96-103. DOI： 10.11959/j.issn.1000-436x.2020095.

摘要

针对声音信号受实际环境噪声影响引起耳蜗倒谱系数（CFCC）波动是导致击键内容识别率低的主要原因，研究相邻键CFCC之间的空间特征，建立基于点的CFCC空间梯度结构；在此基础上，在训练集和测试上研究CFCC空间梯度对击键内容识别的影响及确切邻域的点选取；最后，建立基于声音的高稳健性击键内容识别方法。在不同环境下进行的实验表明，所提CFCC空间梯度声音特征效果较好，识别准确率为96.15% 。

Abstract

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection

the spatial characteristics of adjacent between CFCC were studied

and the spatial gradient structure of CFCC based on points was established.On this basis

the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally

a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.

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references

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