指挥控制与仿真2024，Vol.46Issue(2)：150-156,7.DOI:10.3969/j.issn.1673-3819.2024.02.021
基于深度学习的枪声联合识别定位
Joint recognition and localization of gunshot based on deep learning
摘要
Abstract
In response to the existing gun sound recognition and positioning tasks,which require separate identification and positioning,resulting in time-consuming computation,system redundancy,and complex development processes,this paper proposes to use a two-stage CRNN deep learning network model to complete the gun sound recognition and positioning tasks.Firstly,perform a logarithmic Mel transform on the collected gunshot signal and calculate the generalized phase transition cross correlation spectrum as input to the network model.Secondly,in the first stage,the gunshot signal is identified through the CRNN network.Finally,in the second stage,the introduction of a mask is used to determine whether the CRNN network weight sharing is implemented for localization.The method proposed in this article can effectively solve the problems of sepa-rate recognition and positioning tasks,system redundancy,and complex development processes in traditional methods,and has certain application value in achieving joint recognition and positioning.关键词
联合识别定位/枪声定位/深度学习Key words
joint identification and positioning/gunshot positioning/deep learning分类
军事科技引用本文复制引用
马明星,李剑,曾援,贺斌,庞润嘉..基于深度学习的枪声联合识别定位[J].指挥控制与仿真,2024,46(2):150-156,7.基金项目
国家自然基金青年科学基金(61901419) （61901419）
