End-to-end Recording Device Identification Based on Deep Representation Learning

TL;DR

This paper introduces an end-to-end deep learning framework that fuses spatial and temporal features for recording device identification, improving accuracy over previous methods by fully utilizing source information.

Contribution

It presents a novel fusion approach combining spatial and temporal features with attention mechanisms in an end-to-end deep learning model for device identification.

Findings

Outperforms previous work and baseline systems

Effectively combines spatial and temporal features

Improves identification accuracy under general conditions

Abstract

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.