基于神经网络的射击检测线性预测系数分析

2017 IEEE 26th International Symposium on Industrial Electronics (ISIE) Pub Date : 2017-06-01 DOI:10.1109/ISIE.2017.8001552

M. Hrabina

{"title":"基于神经网络的射击检测线性预测系数分析","authors":"M. Hrabina","doi":"10.1109/ISIE.2017.8001552","DOIUrl":null,"url":null,"abstract":"This work deals with analysis of the linear predictive coefficients with respect to their use for acoustic gunshot detection. First, coefficient stability was observed when changing length and position of an analysed signal frame. Then, the optimal prediction order was investigated. Finally, false alarms and correct gunshot detections were tested for various numbers of coefficients. The best experimental results achieved were 8.6% false alarm rate and 88% gunshot detection rate.","PeriodicalId":6597,"journal":{"name":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","volume":"7 1","pages":"1961-1965"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Analysis of linear predictive coefficients for gunshot detection based on neural networks\",\"authors\":\"M. Hrabina\",\"doi\":\"10.1109/ISIE.2017.8001552\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work deals with analysis of the linear predictive coefficients with respect to their use for acoustic gunshot detection. First, coefficient stability was observed when changing length and position of an analysed signal frame. Then, the optimal prediction order was investigated. Finally, false alarms and correct gunshot detections were tested for various numbers of coefficients. The best experimental results achieved were 8.6% false alarm rate and 88% gunshot detection rate.\",\"PeriodicalId\":6597,\"journal\":{\"name\":\"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)\",\"volume\":\"7 1\",\"pages\":\"1961-1965\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIE.2017.8001552\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIE.2017.8001552","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 7

摘要

这项工作涉及的线性预测系数的分析，就其用于声学射击检测。首先，在分析信号帧的长度和位置变化时观察到系数的稳定性。然后，研究了最优预测顺序。最后，对不同数量的系数进行了假报警和正确射击检测。最佳实验结果为虚警率8.6%，枪响检出率88%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Analysis of linear predictive coefficients for gunshot detection based on neural networks

This work deals with analysis of the linear predictive coefficients with respect to their use for acoustic gunshot detection. First, coefficient stability was observed when changing length and position of an analysed signal frame. Then, the optimal prediction order was investigated. Finally, false alarms and correct gunshot detections were tested for various numbers of coefficients. The best experimental results achieved were 8.6% false alarm rate and 88% gunshot detection rate.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)

自引率

0.00%

发文量