Performance Enhancement of Speech Scrambling Techniques Based on Many Chaotic Signals

机译：基于许多混沌信号的语音加扰技术的性能增强

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

scrambling method structure consists of three main components: scrambling, noisy channel and descrambling. The scrambling and descrambling processes. On the basis of chaotic signals, a speech scrambling technique is an essential work to upgrade and improv

机译：加扰方法的结构由三个主要部分组成：加扰，噪声信道和加扰。加扰和解扰过程。在混沌信号的基础上，语音加扰技术是升级和即兴创作的必不可少的工作。

著录项

来源
《International Conference on Computer Science and Software Engineering》|2020年|308-313|共6页
会议地点
作者
Ammar M. Raheema; Sattar B. Sadkhan-SMIEEE; Sinan Majid Abdul Satar;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类
关键词
Chaos based cryptography; Speech scrambling; AWGN; and Statistical evaluation measurement;

机译：基于混沌的密码学，语音加扰，AWGN和统计评估测量;

相似文献

外文文献
中文文献
专利

1. A STATISTICAL ANALYSIS ON THE IMPACT OF SPEECH ENHANCEMENT TECHNIQUES ON THE FEATURE VECTORS OF NOISY SPEECH SIGNALS FOR SPEECH RECOGNITION [J] . SWAPNANIL GOGOI, UTPAL BHATTACHARJEE Journal of computer science engineering and information technology research . 2016,第3期

机译：语音增强技术对语音识别中嘈杂语音信号特征向量影响的统计分析
2. A STATISTICAL ANALYSIS ON THE IMPACT OF SPEECH ENHANCEMENT TECHNIQUES ON THE FEATURE VECTORS OF NOISY SPEECH SIGNALS FOR SPEECH RECOGNITION [J] . SWAPNANIL GOGOI, UTPAL BHATTACHARJEE Journal of computer science engineering and information technology research . 2016,第3期

机译：语音增强技术对语音识别中嘈杂语音信号特征向量影响的统计分析
3. Speech Scrambling Based on Imitation of a Target Speech Signal with Non-confidential Content [J] . Dora M. Ballesteros L, Juan M. Moreno A Circuits, systems, and signal processing . 2014,第11期

机译：基于具有非机密内容的目标语音信号的语音扰码
4. Performance Comparison of Hybrid Chaotic Maps Based on Speech Scrambling for OFDM Techniques [C] . Ammar M. Raheema, Sattar B. Sadkhan, Sinan M. Abdul Sattar Scientific Conference of Electrical Engineering . 2018

机译：OFDM技术中基于语音加扰的混合混沌映射的性能比较
5. Robust Recognition of Binaural Speech Signals Using Techniques Based on Human Auditory Processing [D] . Menon, Anjali I. 2019

机译：基于人类听觉处理技术的双耳语音信号的稳健识别
6. Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals [O] . Hariharan Muthusamy, Kemal Polat, Sazali Yaacob -1

机译：基于粒子群优化的特征增强和特征选择用于语音和声门信号中的情感识别
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

Performance Enhancement of Speech Scrambling Techniques Based on Many Chaotic Signals

摘要

著录项

相似文献

相关主题

期刊订阅