基于优化的正交匹配追踪声音事件识别

针对各种环境声对声音事件识别的影响，该文提出一种基于优化的正交匹配追踪(Orthogonal Matching Pursuit, OMP)声音事件识别方法。首先，利用OMP稀疏分解并重构声音信号，保留声音信号的主体部分，减小噪声的影响。其中，使用粒子群(Particle Swarm Optimization, PSO)算法优化搜索最优原子，实现OMP的快速稀疏分解。接着，对重构声音信号提取Mel频率倒谱系数(Mel-Frequency Cepstral Coefficients, MFCCs)，与OMP时-频特征和基频(PITCH)特征，组成优化OMP的复合特征。最后，通过优化OMP复合特征，使用随机森林(Random Forests, RF)对40种声音事件在不同环境不同信噪比下进行识别。实验结果表明，优化OMP复合特征结合RF的方法能有效地识别各种环境下的声音事件。

Sound Event Recognition Based on Optimized Orthogonal Matching Pursuit

A sound event recognition method based on optimized Orthogonal Matching Pursuit (OMP) is proposed for decreasing the influence of sound event recognition on various environments. Firstly, OMP is used for sparse decomposition and reconstruction of sound signal to decrease the influence of noise and reserve the main body of sound signal, where Particle Swarm Optimization (PSO) is adopted to accelerate the best atom searching in the process of sparse decomposition. Then, an optimized composited feature of Mel-Frequency Cepstral Coefficients (MFCCs), time-frequency OMP feature, and PITCH feature is extracted from reconstructed signal. Finally, Random Forests (RF) classifier is employed to recognize 40 classes of sound events in different environments and Signal-to-Noise Rates (SNRs). The experiment result shows that the proposed method can effectively recognize sound events in various environments.