Progressive audio scrambling via complete binary tree's traversal and wavelet transform

Twe Ta Oo, T. Onoye
{"title":"Progressive audio scrambling via complete binary tree's traversal and wavelet transform","authors":"Twe Ta Oo, T. Onoye","doi":"10.1109/APSIPA.2014.7041525","DOIUrl":null,"url":null,"abstract":"In this paper, we firstly propose an effective audio scrambling method based on the pre-order traversal of a complete binary tree in time domain. The proposed method is fast, simple and has good scrambling effect. Then, with the aim of strengthening the anti-decryption capability, we present a wavelet-domain based scheme by considering not only the pre-order but also the in-/post-order based scrambling methods. First, an audio signal is wavelet-decomposed and retrieves the layers of wavelet coefficients. Then, the coefficients in each layer are scrambled by randomly chosen one out of the three methods. Anyone without knowledge of the correct wavelet decomposition parameters and the scrambling method used for each layer will never be able to descramble the signal successfully. Moreover, the new scheme also achieves progressive scrambling that enables to generate the audio outputs with different quality levels by controlling the scrambling degree as required.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSIPA.2014.7041525","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

In this paper, we firstly propose an effective audio scrambling method based on the pre-order traversal of a complete binary tree in time domain. The proposed method is fast, simple and has good scrambling effect. Then, with the aim of strengthening the anti-decryption capability, we present a wavelet-domain based scheme by considering not only the pre-order but also the in-/post-order based scrambling methods. First, an audio signal is wavelet-decomposed and retrieves the layers of wavelet coefficients. Then, the coefficients in each layer are scrambled by randomly chosen one out of the three methods. Anyone without knowledge of the correct wavelet decomposition parameters and the scrambling method used for each layer will never be able to descramble the signal successfully. Moreover, the new scheme also achieves progressive scrambling that enables to generate the audio outputs with different quality levels by controlling the scrambling degree as required.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过完全二叉树遍历和小波变换实现音频累进置乱
本文首先提出了一种有效的音频置乱方法,该方法基于时间域完全二叉树的预序遍历。该方法快速、简便,置乱效果好。然后,为了增强抗解密能力,我们提出了一种基于小波域的方案,该方案既考虑了前序加扰,又考虑了后序加扰方法。首先,对音频信号进行小波分解,提取小波系数层。然后,从三种方法中随机选择一种方法对每一层的系数进行置乱。任何不知道正确的小波分解参数和每层使用的加扰方法的人都无法成功地解扰信号。此外,该方案还实现了累进置乱,可以根据需要控制置乱程度,从而产生不同质量水平的音频输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Smoothing of spatial filter by graph Fourier transform for EEG signals Intra line copy for HEVC screen content coding Design of FPGA-based rapid prototype spectral subtraction for hands-free speech applications Fetal ECG extraction using adaptive functional link artificial neural network Opened Pins Recommendation System to promote tourism sector in Chiang Rai Thailand
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1