基于语音频谱融合特征的手机来源识别  

Cell-phone source identification based on spectral fusion features of recorded speech

作　　者：裴安山 王让定[1] 严迪群[1] 

机构地区：[1]宁波大学信息科学与工程学院,浙江宁波315211

出　　处：《计算机应用》2018年第3期884-890,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61672302;61300055);浙江省自然科学基金资助项目(LZ15F020002;LY17F020010);宁波市自然科学基金资助项目(2017A610123);宁波大学科研基金资助项目(XKXL1509;XKXL1503)~~

摘　　要：随着手机录音设备的普及以及各种功能强大且易于操作的数字媒体编辑软件的出现,语音的手机来源识别已成为多媒体取证领域重要的热点问题,针对该问题提出了一种基于频谱融合特征的手机来源识别算法。首先,通过分析不同手机相同语音的语谱图,发现不同手机的语音频谱特征是不同的;然后对语音的频谱信息量、对数谱和相位谱特征进行了研究;其次,将三个特征串联构成原始融合特征,并用每个样本的原始融合特征构建样本特征空间;最后,采用WEKA平台的CfsSubsetEval评价函数按照最佳优先搜索原则对所构建的特征空间进行特征选择,并采用LibSVM对特征选择后的样本特征空间进行模型训练和样本识别。实验部分给出了特征选择后的频谱单一特征和频谱融合特征在23款主流型号的手机语音库上分类的结果。实验结果表明,该算法使用频谱融合特征有效提高了手机品牌类内的平均识别准确率,在TIMIT翻录语音数据库和自建的CKC-SD语音数据库上分别达到99.96%和99.91%;另外,与Hanilci基于梅尔倒谱系数特征的录音设备来源识别算法进行了对比,平均识别准确率分别提高了6.58和5.14个百分点。因此可得本文所提特征可有效提高平均识别准确率,降低手机类内识别的误判率。With the popularity of cell-phone recording devices and the availability of various powerful and easy to operate digital media editing software, source cell-phone identification has become a hot topic in multimedia forensics, a cell-phone source recognition algorithm based on spectral fusion features was proposed to solve this problem. Firstly, the same speech spectrograms of different cell-phones were analyzed, it was found that the speech spectral characteristics of different cell- phones were different; then the logarithmic spectrum, phase spectrum and information quantity for a speech were researched. Secondly, the three features were connected in series to form the original fusion feature, and the sample feature space was constructed with the original fusion feature of each sample. Finally, the evaluation function CfsSubsetEval of WEKA platform was selected according to the best priority search method to select features, and LibSVM was used to model training and sample recognition after feature selection. Twenty-three popular cell-phone models were evaluated in the experiment, the results showed that the proposed spectral fusion feature has higher identification accuracy for cell-phone brands than spectral single feature and the average identification accuracies achieved 99.96% and 99.91% on TIMIT database and CKC-SD database. In addition, it was compared with the source identification algorithm of Hanilei based on Mel frequency cepstral coefficients, the average identification accuracy was improved by 6.58 and 5.14 percentage points respectively. Therefore, the proposed algorithm can improve the average identification accuracy and effectively reduce the false positives rate of cell-phone source identification.

关 键 词：多媒体取证 手机来源识别 频谱融合特征 特征选择 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]