针对公共环境中的声音事件识别问题,提出基于自适应粒子群优化(PSO)匹配追踪(MP)稀疏分解的声音事件识别算法。该算法在分析MP稀疏分解的基础上,先基于适应度函数改进PSO算法相关参数的自适应设置,再基于自适应PSO算法构建优化MP稀疏分解的目标函数及信号重构函数,实现自适应PSO算法优化MP稀疏分解,从而借助连续Gabor超完备集来提高最优原子的匹配程度,增强声音信号,提高特征的分类性能,最后使用优化的支持向量机(SVM)和复合特征实现公共环境中的声音事件准确识别。实验结果表明,与已有算法相比,所提识别算法显著降低了计算量,并取得了最优的声音识别率,且具有较好的识别鲁棒性。

A new algorithm based on optimized match pursuit (MP) sparse decomposition using adaptive particle swarm optimization (PSO) is proposed to address the recognition problem of sound events in public environment. Based on MP sparse decomposition analysis, the fitness function was used to improve the adaptive setting of parameters related to PSO algorithm; then, the objective function and signal reconstruction function were constructed for optimizing sparse decomposition, thus realizing adaptive PSO algorithm optimized MP sparse decomposition. Moreover, the continuous Gabor super complete set was used to improve the matching degree of the optimal atom, which enhanced the sound signal and improved the classification performance of the feature. Finally, optimized support vector machine (SVM) and composite features were used to achieve accurate recognition of sound events in public environments. Experimental results show that the proposed algorithm significantly reduces computational complexity, achieves optimal recognition rate, and demonstrates better robustness compared with existing algorithms.