基于语音静音段特征的手机来源识别方法

doi:10.11959/j.issn.1000-0801.2017123

电信科学 ›› 2017, Vol. 33 ›› Issue (7): 103-111.doi: 10.11959/j.issn.1000-0801.2017123

基于语音静音段特征的手机来源识别方法

裴安山,王让定(),严迪群

宁波大学信息科学与工程学院，浙江宁波 315211

修回日期:2017-03-30 出版日期:2017-07-01 发布日期:2017-07-25
作者简介:裴安山（1992?），男，宁波大学信息科学与工程学院硕士生，主要研究方向为多媒体通信、信息安全、移动终端来源检测等。|王让定（1962?），男，博士，宁波大学信息科学与工程学院教授、博士生导师，主要研究方向为多媒体通信与取证、信息隐藏与隐写分析、智能抄表及传感网络技术等。|严迪群（1979?），男，博士，宁波大学信息科学与工程学院副教授、硕士生导师，主要研究方向为多媒体通信、信息安全、基于深度学习的数字语音取证等。
基金资助:
国家自然科学基金资助项目(61672302);国家自然科学基金资助项目(61300055);浙江省自然科学基金资助项目(LZ15F020002);浙江省自然科学基金资助项目(LY17F020010);宁波大学科研基金资助项目(XKXL1405);宁波大学科研基金资助项目(XKXL1420);宁波大学科研基金资助项目(XKXL1509);宁波大学科研基金资助项目(XKXL1503);宁波大学王宽诚幸福基金资助项目

Source cell-phone identification from recorded speech using non-speech segments

Anshan PEI,Rangding WANG(),Diqun YAN

College of Information Science and Engineering,Ningbo University,Ningbo 315211,China

Revised:2017-03-30 Online:2017-07-01 Published:2017-07-25
Supported by:
The National Natural Science Foundation of China(61672302);The National Natural Science Foundation of China(61300055);Natural Science Foundation of Zhejiang Province of China(LZ15F020002);Natural Science Foundation of Zhejiang Province of China(LY17F020010);The Scientific Research Foundation of Ningbo University(XKXL1405);The Scientific Research Foundation of Ningbo University(XKXL1420);The Scientific Research Foundation of Ningbo University(XKXL1509);The Scientific Research Foundation of Ningbo University(XKXL1503);K.C.Wong Magna Fund in Ningbo University

摘要/Abstract

摘要：

手机来源识别已成为多媒体取证领域重要的热点问题。提出了一种基于语音静音段特征的手机来源识别方法，该方法先通过使用自适应端点检测算法得到语音的静音段；然后将静音段的梅尔频谱系数（MFC）的均值作为分类特征；最后结合 WEKA 平台的CfsSubsetEval评价函数按照最佳优先（BestFirst）搜索进行特征选择，并采用支持向量机（SVM）对手机来源进行识别。实验部分对23款主流型号的手机进行了分类，结果表明所提特征具有较好的分类性能，在TIMIT数据库和自建的CKC-SD数据库上，平均识别准确率分别为99.23%和99.00%。另外，与语音段MFC特征和梅尔倒谱系数（MFCC）特征进行了对比，实验结果证明所提特征具有更加优越的性能。

关键词: 多媒体取证, 手机来源识别, 静音段, 梅尔频谱特征

Abstract:

Source cell-phone identification has become a hot topic in multimedia forensics.A novel cell-phone identification method was proposed based on the silent segments of recorded speech.Firstly,the silent segments were obtained using adaptive endpoint detection algorithm.Then,the mean of Mel frequency coefficients (MFC) was extracted as the characteristics for device identification.Finally,the CfsSubsetEval evaluation function of WEKA platform was selected according to the best priority (BestFirst) search,and support vector machine (SVM) was used for classification.Twenty-three popular models of the cell-phones were evaluated in the experiment.Experimental results show that the proposed method is feasible and the average recognition rates are 99.23% and 99.00% on the TIMIT database and the CKC-SD database.At the same time,the proposed feature performs was demonstrated better than the MFC features and the Mel frequency cepstrum coefficients (MFCC) features of the speech segments.

Key words: audio forensics, source cell-phone identification, silent segment, Mel frequency coefficient

中图分类号:

TP391

裴安山,王让定,严迪群. 基于语音静音段特征的手机来源识别方法[J]. 电信科学, 2017, 33(7): 103-111.

Anshan PEI,Rangding WANG,Diqun YAN. Source cell-phone identification from recorded speech using non-speech segments[J]. Telecommunications Science, 2017, 33(7): 103-111.

图/表 8

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类名	品牌	型号	类名	品牌	型号
H1	HTC	D610t	A1	苹果	iPhone 4s
H2		D820t	A2		iPhone 5
H3		One M7	A3		iPhone 5s
W1	华为	Honor6	A4		iPhone 6
W2		Honor7	A5		iPhone 6s
W3		Mate7	Z1	魅族	Meilan Note
O1	OPPO	Find7	Z2		MX2
O2		Oneplus1	Z3		MX4
O3		R831S	M1	小米	Mi 3
S1	三星	Galaxy Note2	M2		Mi 4
S2		Galaxy S5	M3		红米Note1
S3		Galaxy GT-I8558

AL (标签)	识别准确率
AL (标签)	H₁	H₂	H₃	W₁	W₂	W₃	A₁	A₂	A₃	A₄	A₅	Z₁	Z₂	Z₃	M₁	M₂	M₃	Q₁	Q₂	Q₃	S₁	S₂	S₃
H1	97.2%	2.8%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
H2	5.8%	94.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
H3	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W1	0	0	0	99.8%	0.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W2	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W3	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A1	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A2	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A3	0	0	0	0	0	0	0	0	98.6	0.4%	0	0	0	0	0	0	0	0	0	0	0	0	0
A4	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0
A5	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0
Z1	0	0	0	0	0	0	0	0	0	0	0	95.6%	0	4.4%	0	0	0	0	0	0	0	0	0
Z2	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0
Z3	0	0	0	0	0	0	0	0	0	0	0	3.2%		96.8%	0	0	0	0	0	0	0	0	0
M1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0
M2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0
M3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0
Q1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0
Q2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0
Q3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0
S1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0
S2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100	0
S3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%

AL (标签)	识别准确率
AL (标签)	H₁	H₂	H₃	W₁	W₂	W₃	A₁	A₂	A₃	A₄	A₅	Z₁	Z₂	Z₃	M₁	M₂	M₃	Q₁	Q₂	Q₃	S₁	S₂	S₃
H1	95.8%	4.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
H2	2.2%	97.4%	0.2%	0	0	0	0	0	0	0	0	0	0	0.2%	0	0	0	0	0	0	0	0	0
H3	0	0	99.4%	0.4%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.2%	0	0
W1	0	0	0.4%	98.2%	1.2%	0	0	0	0	0	0	0	0	0	0	0.2%	0	0	0	0	0	0	0
W2	0	0	0	1.6%	98%	0	0	0	0.2%	0	0	0	0	0	0	0.2%	0	0	0	0	0	0	0
W3	0	0	0	0.6%	0.4%	98.8%	0	0	0	0	0	0	0	0.2%	0	0	0	0	0	0	0	0	0
A1	0	0	0	0	0	0	99.4%	0	0.4%	0	0	0	0	0	0	0	0	0	0	0	0.2%	0	0
A2	0	0	0.2%	0	0	0	0	99.4%	0.2%	0	0	0	0	0	0	0	0	0	0	0	0.2%	0	0
A3	0	0	0	0	0.2%	0	0.2%	0	98.2%	1.4%	0	0	0	0	0	0	0	0	0	0	0	0	0
A4	0	0	0	0	0	0	1%	0	1.4%	97.6%	0	0	0	0	0	0	0	0	0	0	0	0	0
A5	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0
Z1	0	0	0	0.2%	0	0	0	0	0	0	0	99.4%	0.2%	0	0	0.2%	0	0	0	0	0	0	0
Z2	0	0	0	0	0	0	0	0	0	0	0	0	99.8%	0	0	0	0	0	0.2	0	0	0	0
Z3	0	0	0	0	0	0	0	0	0	0	0	0	0	99.6%	0	0	0	0	0	0	0	0	0
M1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	99.6%	0	0	0	0	0	0	0	0.4%
M2	0	0	0	0.2%	0	0	0	0	0	0	0	0	0	0	0	99.8%	0	0	0	0	0	0	0
M3	0	0	0.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	99.6%	0	0	0.2%	0	0	0
Q1	0.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	99.2%	0	0	0.6	0	0
Q2	0	0	0	0	0	0	0	0	0	0	0	0.2%	0.2%	0	0	0	0	0	99.6%	0	0	0	0
Q3	0	0	0	0	0	0	0	0	0.4%	0	0	0	0	0	0	0	0	0	0	99.6%	0	0	0
S1	0	0	0.2%	0	0	0	0	0.2%	0	0	0	0	0	0	0.2%	0	0	0	0	0	99.2%	0.2%	0
S2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.4%	99.6%	0
S3	0	0	0.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	99.8%

特征库	相同型号相同设备的语音手机来源识别准确率	相同型号不同设备的语音手机来源识别准确率
TIMIT库	99.23%	98.13%

特征库	参考文献[13]算法	本文算法（静音段MFC特征）
TIMIT库	93.38%	99.23%
CKC-SD库	94.77%	99.00%

基于语音静音段特征的手机来源识别方法

Source cell-phone identification from recorded speech using non-speech segments

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