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

作　　者：马明星 李剑[1,2] 曾援 贺斌 庞润嘉 MA Mingxing;LI Jian;ZENG Yuan;HE Bin;PANG Runjia(State Key Laboratory of Dynamic Testing Technology,North University of China,Taiyuan 030051,China;Key Laboratory of Information Detection and Processing of ShanxiProvince,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学省部共建动态测试技术国家重点实验室,山西太原030051 [2]中北大学信息探测与处理山西省重点实验室,山西太原030051

出　　处：《指挥控制与仿真》2024年第2期150-156,共7页Command Control & Simulation

基　　金：国家自然基金青年科学基金(61901419)。

摘　　要：针对现有枪声识别与定位任务中,识别与定位需分别进行,造成计算耗时、系统冗余、开发流程复杂等问题,提出使用一个Two Stage CRNN深度学习网络模型处理枪声识别与定位任务。首先,对采集到的枪声信号进行对数梅尔变换并计算广义相变互相关谱作为网络模型输入;其次,第一阶段通过CRNN网络对枪声信号进行识别;最后,第二阶段通过引入掩码实现判断是否将CRNN网络权重共享实现定位。相关实验表明,此方法能有效解决传统方法中识别与定位任务分别实现、系统冗余、开发流程复杂的问题,在实现联合识别定位中具有一定的应用价值。In response to the existing gun sound recognition and positioning tasks,which require separate identification and positioning,resulting in timeconsuming computation,system redundancy,and complex development processes,this paper proposes to use a twostage 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 separate 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 

分 类 号：E912[军事]