Spatial and temporal learning representation for end-to-end recording device identification

EURASIP journal on advances in signal processing

article

Zeng, Chunyan¹  Zhu, Dongliang¹  Wang, Zhifeng²  Wu, Minghu¹  Xiong, Wei¹  Zhao, Nan¹ 

[1] Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology;Department of Digital Media Technology, Central China Normal University

关键词: Spatial features;    Temporal features;    Convolution neural network (CNN);    Long Short-Term Memory (LSTM);   

DOI  :  10.1186/s13634-021-00763-1

来源: SpringerOpen

【 摘 要 】

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.

【 授权许可】

CC BY