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VPCID - A VoIP Phone Call Identification Database

机译:VPCID - VoIP电话识别数据库

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Audio forensic plays an important role in the field of information security to address disputes related to the authenticity and originality of audio. However, some audio forensics methods presented in existing references were evaluated under either non-forensic oriented databases or private databases which were not publicly available. It creates difficulty for researchers to make comparison between different methods. In this paper we established VPCID, a VoIP phone call identification database for audio forensic purpose. As there is an increasing trend of phone scams or voice phishing via VoIP, through which the caller's identity can be hidden or forged easily, it is demanded to address the issues of identifying VoIP phone calls. The VPCID database is comprising of 1152 VoIP call recordings and 1152 mobile phone call recordings, each of which has more than two minutes. Recordings were collected from 48 different speakers using different smart phones and by considering varies recording conditions such as VoIP software, locations etc. We used MFCC (Mel-Frequency Cepstral Coefficients) and ACV (Amplitude Co-occurrence Vector) based features respectively equipped with SVM (Support Vector Machine) classifier to perform classification on the database. We also evaluated our own database on a CNN (convolutional neural network), but the performance is not too much satisfactory. Therefore the VoIP phone call identification problem is challenging and it calls for more effective solutions to address the problem. We hope our proposed database will convey more than this paper and inspire the future studies, which is openly available in below link, http://media-sec.szu.edu.cn/VPCID. html, and we welcome the use of this database.
机译:音频取证在信息安全领域起着重要作用,以解决与音频的真实性和原创性相关的争议。但是,在未上取向的数据库或私人数据库下评估了现有参考中的一些音频取证方法,这些方法或未公开可用的私人数据库。研究人员对不同方法进行比较创造了困难。在本文中,我们建立了VPCID,一个VoIP电话识别数据库,用于音频法医用途。由于手机诈骗或通过VoIP的语音网络趋势越来越大,因此可以轻松地隐藏或伪造调用者的身份,需要解决识别VoIP电话的问题。 VPCID数据库包括1152个VoIP呼叫记录和1152个手机呼叫记录,每个移动电话录音有超过两分钟。使用不同的智能手机的48个不同扬声器收集录音,并考虑各种录音条件,如VoIP软件,地点等。我们使用了基于MFCC(Mel-usion患者穴位数)和ACV(振幅共存向量)的基于SVM的特征(支持向量机)分类器在数据库上执行分类。我们还在CNN(卷积神经网络)上评估了我们自己的数据库,但性能不是太多令人满意。因此,VoIP电话识别问题是具有挑战性的,它要求更有效的解决方案来解决问题。我们希望我们的拟议数据库能够超过本文,并激发未来的研究,该研究在下面的链接中公开可用,http://media-sec.szu.edu.cn/vpcid。 HTML,我们欢迎使用此数据库。

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