一种强噪声干扰下的炮控系统声音识别算法
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摘要
声音作为一种重要的信息媒介,能够为维修人员提供大量的装备信息;但实际维修环境受到车辆启动噪声的干扰,难以准确直观地对声音进行判断;为实现对炮控系统各主要声音部组件启动过程的识别,提出了一种基于改进谱减法降噪和多类型识别策略的声音识别算法;通过对炮控系统各部组件与发动机声音信号的分析,利用改进谱减法对声音样本进行了降噪处理,并通过实验优化了谱减参数,进一步提升了降噪性能,解决了强噪声干扰的问题;利用滑窗校正和短时能量同步检测的方法制定了具体的识别策略,解决了实际应用中识别结果不稳定以及多类型过程识别的问题;通过实验验证,该声音识别算法对炮控系统各部件启动状态识别准确率达92.4%,具有较好的识别性能。
However,the actual maintenance environment is disturbed by vehicle starting noise,so it is difficult to accurately and intuitively judge the sound.In order to recognize the start-up process of the main sound components of gun control system,a sound recognition algorithm based on improved spectral subtraction noise reduction and multi-type recognition strategy was proposed.Through the analysis of the sound signals of gun control system components and engine,the improved spectral subtraction method is used to reduce the noise of sound samples,and the spectral subtraction parameters are optimized through experiments,which further improves the performance of noise reduction and solves the problem of strong noise interference.A specific recognition strategy is developed by using sliding window correction and short-time energy synchronization detection,which solves the problems of unstable recognition results and multi-type process identification in practical application.Experiments show that the recognition accuracy of this algorithm is 92.4%for the start-up status of each component of gun control system,and it has good recognition performance.
However,the actual maintenance environment is disturbed by vehicle starting noise,so it is difficult to accurately and intuitively judge the sound.In order to recognize the start-up process of the main sound components of gun control system,a sound recognition algorithm based on improved spectral subtraction noise reduction and multi-type recognition strategy was proposed.Through the analysis of the sound signals of gun control system components and engine,the improved spectral subtraction method is used to reduce the noise of sound samples,and the spectral subtraction parameters are optimized through experiments,which further improves the performance of noise reduction and solves the problem of strong noise interference.A specific recognition strategy is developed by using sliding window correction and short-time energy synchronization detection,which solves the problems of unstable recognition results and multi-type process identification in practical application.Experiments show that the recognition accuracy of this algorithm is 92.4%for the start-up status of each component of gun control system,and it has good recognition performance.
引文
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[2]颜延,邹浩,周林,等.可穿戴技术的发展[J].中国生物医学工程学报,2015,34(6):644-653.
[3]陈东义,夏侯士戟,黄志奇,等.面向工业应用的可穿戴计算技术[J].电子科技大学学报,2009,38(5):678-686.
[4]曾锐利,肖云魁,曹亚娟,等.基于穿戴式计算机的数字化维修单兵系统[J].微计算机信息,2009,25(13):172-174.
[5]韩纪庆.声学事件检测技术的发展历程与研究进展[J].数据采集与处理,2016,31(2).
[6]杨宇.面向可穿戴计算机音频的环境感知系统的研究[D].哈尔滨:哈尔滨工业大学,2014.
[7]张赛花,赵兆,许志勇,等.基于Mel子带参数化特征的自动鸟鸣识别[J].计算机应用,2017,37(4):1111-1115.
[8]易子馗,谭建平,刘思思.基于改进谱减法和MFCC的电机异常噪声识别方法[J].微特电机,2017,45(2):31-38.
[9]李颀.基于小波包分析的玻璃破碎声音识别系统设计[J].计算机测量与控制,2018(1):168-172.
[10]刘波霞,陈建峰.基于特征分析的环境声音事件识别算法[J].计算机工程,2011,37(22):261-263.