基于声音信号的键盘组合键击键内容的精确识别

发布时间：2018-02-05 20:34

本文关键词： 组合键检测声音识别盲源信号分离智能手机　出处：《深圳大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着声音定位技术以及窃听技术的发展,击键内容的识别的研究已经受到工业界和学术界的关注。随着智能手机的发展,尤其是手机上各种传感器性能的提升,智能设备已经被广泛地应用在定位问题中以实现厘米级别的定位。根据信息安全的原理,窃听技术的研究能够有效地阻止识别用户的击键内容。现有用来解决键盘击键内容的识别主要集中在以下三类:基于WiFi信号,基于计算机视觉,基于声音信号。基于WiFi信号,利用商业无线路由设备和无线网卡来检测用户击键的手势,主要原理是检测手势和无线网络信道之间的关系;基于摄像头和计算机视觉技术来识别击键内容,这种方案的缺点是受光照条件影响;基于声音的键盘击键内容的识别,通过收集敲击键盘的声音并分析声音的特征来识别不同的键。基于声音信号的方法,具有很强的分辨率,同时,现有的键盘检测的方法很少用于键盘组合键的检测。为了解决上述问题,本文提出了一种基于独立主成分算法的组合键击键内容的识别的方法。本文进行了一些实验,来验证之前方法在组合键研究上的不适用,同时验证主成分分析方法在组合键研究的可行性。以下三个部分,是本文的主要研究工作:第一,阅读相关论文,了解研究现状。特别地,对声音盲源信号分析以及独立成分分析技术的学习,了解传统的基于WiFi定位方法策略以及声源定位方法。第二,设计实验,采集数据,部署好实验场景。在比较安静的环境中进行实验并运用技术手段对实验数据进行降噪,尽可能地减少因环境或人员偶然操作等因素产生的噪声。第三,特征提取,键盘击键内容检测。利用FastICA算法对双麦克风接收到的混合信号进行分离,并利用机器学习方法进行分类,识别出击键内容。实验结果表明,本文采取的智能手机这一方案能够有效地检测组合键。实验结果显示,平均准确率78.4%。本文主要的创新点在于首次利用声音信号盲源分离技术进行组合键的研究,本文设计的系统简单,只需要手机,容易操作且具有一定的实用性。
[Abstract]:With the development of sound location technology and eavesdropping technology, the research of keystroke content recognition has attracted the attention of industry and academia. With the development of smart phones, especially the improvement of the performance of various sensors on mobile phones. Intelligent devices have been widely used in positioning problems in order to achieve centimeter-level positioning. According to the principle of information security. The research of eavesdropping technology can effectively prevent the identification of keystroke content of users. The existing identification of keystroke content is mainly focused on the following three types: based on WiFi signal and computer vision. Based on sound signal and WiFi signal, commercial wireless routing device and wireless network card are used to detect user keystroke gesture. The main principle is to detect the relationship between gesture and wireless network channel. Keystroke content is identified based on camera and computer vision technology. The disadvantage of this scheme is that it is affected by illumination conditions. The recognition of keystroke content based on sound, by collecting the sound of the keystroke keyboard and analyzing the characteristics of the sound to identify different keys. Based on the sound signal method, it has a strong resolution and at the same time. The existing methods of keyboard detection are rarely used for keyboard key combination detection. In order to solve the above problems. In this paper, an independent principal component algorithm based on the key combination keystroke content recognition method. Some experiments are carried out to verify the previous method in combination key research is not applicable. At the same time verify the feasibility of the principal component analysis method in the study of key combination. The following three parts are the main research work of this paper: first, read the related papers, understand the research status. In particular. To the sound blind source signal analysis and independent component analysis technology learning, to understand the traditional localization method based on WiFi and sound source location methods. Second, design experiments, collect data. Deployment of the experimental scene. In a quiet environment to experiment and use technical means to reduce the noise of experimental data, as far as possible to reduce the environment or personnel accidental operation and other factors generated by noise. Third. Feature extraction, keyboard keystroke content detection. FastICA algorithm is used to separate the mixed signals received by two microphones, and the machine learning method is used to classify the mixed signals. The experimental results show that the proposed smart phone can effectively detect the key combination. The experimental results show that. The main innovation of this paper is to use blind source separation technology of sound signal for the first time to study key combination. The system designed in this paper is simple and only needs mobile phone. Easy to operate and practical.
【学位授予单位】：深圳大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN912.3

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