  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.
