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.　©　2017,　Science　Press.　All　right　reserved.