Deep learning techniques have achieved specific results in recording device source identification. The recording device source features include spatial information and certain temporal information. However, most recording device source identification methods based on deep learning only use spatial representation learning from recording device source features, which cannot make full use of recording device source information. Therefore, in this paper, to fully explore the spatial information and temporal information of recording device source, we propose a new method for recording device source identification based on the fusion of spatial feature information and temporal feature information by using an end-to-end framework. From a feature perspective, we designed two kinds of networks to extract recording device source spatial and temporal information. Afterward, we use the attention mechanism to adaptively assign the weight of spatial information and temporal information to obtain fusion features. From a model perspective, our model uses an end-to-end framework to learn the deep representation from spatial feature and temporal feature and train using deep and shallow loss to joint optimize our network. This method is compared with our previous work and baseline system. The results show that the proposed method is better than our previous work and baseline system under general conditions.
翻译:深层学习技术在记录设备源识别方面取得了具体成果。记录设备源的特征包括空间信息和某些时间信息。然而,大多数基于深层学习的记录设备源身份识别方法仅使用从记录设备源特征中空间代表学习,无法充分利用记录设备源信息。因此,在本文件中,为了充分探索记录设备源的空间信息和时间信息,我们提出了一个基于使用端至端框架将空间特征信息和时间特征信息融合在一起的记录设备源识别的新方法。从特征角度看,我们设计了两类网络,以提取记录设备源空间和时间信息。之后,我们利用关注机制,根据适应性分配空间信息和时间信息的权重以获取聚合特征。从模型角度看,我们的模型使用端至端框架,从空间特征和时间特征中学习深度代表,并利用深度和浅度损失培训,共同优化我们的网络。这一方法与我们先前的工作和基线系统进行了比较。结果显示,在一般条件下,拟议方法比我们先前的工作和基线系统要好。