版权说明 操作指南
首页 > 成果 > 详情

Spatio-temporal representation learning enhanced source cell-phone recognition from speech recordings

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Zeng, Chunyan;Feng, Shixiong;Wang, Zhifeng;Wan, Xiangkui;Chen, Yunfan;...
通讯作者:
Wang, ZF
作者机构:
[Feng, Shixiong; Zhao, Nan; Chen, Yunfan; Wan, Xiangkui; Zeng, Chunyan] Hubei Univ Technol, Hubei Key Lab High efficiency Utilizat Solar Energ, Wuhan 430000, Peoples R China.
[Wang, Zhifeng] Cent China Normal Univ, Dept Digital Media Technol, Wuhan 430000, Peoples R China.
通讯机构:
[Wang, ZF ] C
Cent China Normal Univ, Dept Digital Media Technol, Wuhan 430000, Peoples R China.
语种:
英文
关键词:
Source cell-phone recognition;Temporal Gaussian feature;Feature representation learning;3D CNN
期刊:
Journal of Information Security and Applications
ISSN:
2214-2126
年:
2024
卷:
80
页码:
103672
基金类别:
CRediT authorship contribution statement Chunyan Zeng: Conceptualization, Methodology, Software, Investigation, Formal analysis, Writing – original draft. Shixiong Feng: Data Curation, Writing – original draft. Zhifeng Wang: Supervision, acquisition, Conceptualization, Methodology, Writing – review & editing. Xiangkui Wan: Software, Validation. Yunfan Chen: Data Curation. Nan Zhao: Resources.
机构署名:
本校为通讯机构
摘要:
The existing source cell-phone recognition method lacks the long-term feature characterization of the source device, resulting in an inaccurate representation of the source cell-phone related features, which leads to insufficient recognition accuracy. In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning. In the feature extraction part, based on the analysis of ...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com