CAJ | 학술논문

现有的数字音频取证技术很难做到录音地点的识别，因此司法机关就不易对音频证据的有效性做出判断．针对现状，本文设计了一种基于BP神经网络的录音地点识别方法．该方法是将电网频率（ ENF）作为识别根据．进行地点识别操作时，首先将电网ENF作为训练样本训练BP神经网络，然后从待取证的音频文件中提取电网频率数据并作为输入样本，用训练好的BP神经网络对输入样本进行识别，最后用模拟退火算法从识别结果中搜索出最佳识别结果，从而识别出录音的地点．实验结果表明，该方法的识别准确率最低达到90．6％，可靠性满足一定的要求．
현유적수자음빈취증기술흔난주도록음지점적식별，인차사법궤관취불역대음빈증거적유효성주출판단．침대현상，본문설계료일충기우BP신경망락적록음지점식별방법．해방법시장전망빈솔（ ENF）작위식별근거．진행지점식별조작시，수선장전망ENF작위훈련양본훈련BP신경망락，연후종대취증적음빈문건중제취전망빈솔수거병작위수입양본，용훈련호적BP신경망락대수입양본진행식별，최후용모의퇴화산법종식별결과중수색출최가식별결과，종이식별출록음적지점．실험결과표명，해방법적식별준학솔최저체도90．6％，가고성만족일정적요구．
The existing digital audio forensics technology has difficulty in identifying the location,where the recordings are made,making it hard for judicial organs to assess the effectiveness of the audio evidence.To address such an issue,this paper devises a method for identifying such locations using a grid ENF based on BP neural network. In the identification process ,grid ENF is used as a training sample for purpose of training BP neural network.Next,the grid frequency data are extracted from audio files as input samples,which are then identified by using the trained BP neural network.Finally,to identify the location of the recording,optimal recognition results are obtained from the recognition results by adopting a simulated annealing algorithm.The experimental results show that recognition rate of this approach is at least 90.6 %,and the approach is reasonably reliable.