Sound　event　recognition　becomes　a　basic　task　in　some　applications.　However,　in　low　SNR　condition,　the　accuracy　rate　is　easily　affected　by　the　acoustic　scene.　To　address　the　problem,　this　paper　proposes　a　framework　consisting　of　empirical　mode　decomposition　(EMD),　gray　level　co-occurrence　matrix　combined　with　higher-order　singular　value　decomposition　(GLCM-HOSVD),　and　random　forests　(RF).　We　use　a　voting　method　based　on　the　first　to　sixth　intrinsic　mode　functions　(IMFs)　which　is　generated　from　EMD,　to　detect　the　endpoint　of　sound　events　and　estimate　the　SNR.　GLCM-HOSVD　is　proposed　to　extract　features　from　audio　data.　During　classifier　training,　the　sound　samples　mixed　by　environmental　sound　and　sound　events　are　used　to　train　RF.　The　experiment　proves　that　the　proposed　method　has　the　ability　to　recognize　low　SNR　sound　events　in　acoustic　scenes.　The　result　shows　that　the　accuracy　rate　is　higher　than　78%　even　in　5dB　acoustic　scene.　©　2015　IEEE.