复杂场景下声频传感器网络核稀疏表示车辆识别

Vehicle recognition using acoustic sensor networks in complex scenes via kernel sparse representation

提出一种基于核稀疏表示的声频传感器网络车辆识别方法.首先利用Mel频率倒谱系数提取车辆声音特征;然后采用核方法将其投影到高维特征空间以实现线性可分,将线性稀疏表示扩展到核域空间,构建过完备字典,求解核稀疏最优化问题对目标车辆进行分类.实验验证了该算法在声频数据集结构复杂的情况下,能有效地识别目标车辆,与传统的声频目标分类算法相比,提高了识别的准确率.

This paper proposes a method of vehicle recognition via kernel sparse representation using acoustic sensor networks in complex scenes . This algorithm uses the Mel frequency cepstral coefficients to extract the acoustic features of vehicles and maps them into a high‐dimensional feature space with a kernel function to get linearly separable samples . After extending sparse representation to the kernel space and constructing the over‐complete dictionary , the objective vehicles will be recognized by solving the optimization problem . Experiments show that the proposed algorithm gives good performance on vehicle recognition under the circumstance of complex data sets . Compared with other traditional acoustic classification algorithms , the method improves the precision of recognition .