基于多尺度子带能量集特征的膛口波识别方法

Muzzle Blast Recognition Based on Multi-scale Subband Energy Set

面向实际复杂环境下的枪声探测应用,提出一种基于多尺度子带能量集特征的膛口波识别方法。采用一组低频子带滤波器增强膛口波信号,通过膛口波检测和波峰搜索确定候选膛口波的起点位置,并基于该起点以嵌套方式截取候选膛口波及其各子带分量的多尺度数据片段,用各片段数据的短时能量和能量比构成多尺度子带能量集特征输入支持向量机和k近邻分类器,进行膛口波和非膛口波识别。对372段外场实录枪声数据进行数值实验,结果表明:所提方法对膛口波识别的查全率、查准率均高于93%,加权调和平均高于0. 95;两种分类器下的识别结果无明显差别,但所用特征维数和计算耗时却远低于常用的离散小波方法,更接近实际应用需求。

A recognition method based on a multi-scale subband energy feature set for muzzle blast is proposed to detect gunshot in real complicated acoustic environment.After muzzle blast signal is enhanced with a group of low-frequency subband filters,the muzzle blast detection followed by a crest search procedure is applied to determine the start position of candidate muzzle blast.Based on the start position,a set of nested data fragments with different lengths are segmented from the candidate muzzle blast and its subband components,and the short-time energies of data fragments and the corresponding energy ratios are then obtained to form the multi-scale subband energy set.The energy set is used as the feature vector for either support vector machine(SVM)or k-nearest neighbor(kNN)classifiers for the classification of muzzle blast and non-muzzle blast.The experimental results with 372 field recordings of gunshot demonstrate that both the precision and recall ratios of the proposed method are larger than 93%,and F 1 score is better than 0.95.The proposed method shows less difference in recognition performances in using both SVM and kNN classifiers,with feature dimension and time consumption being much lower than those of the traditional wavelet-based means,indicating that it is more approaching practical applications.