摘要：Gaussian mixture model-universal background model (GMM-UBM) is a commonly-used speaker recognition technology, and which has achieved good effect for detection speaker’s sound. In this paper, we explore GMM-UBM method for use with abnormal aircraft engine sound detection. We designed a GMM-UBM based aircraft engine sound recognition system, which extracts MFCC feature parameters and trains the GMM-UBM models using maximum a posteriori (MAP) adaptive algorithm. Experimental results show the GMM-UBM based aircraft engine sound recognition system can achieve higher recognize rate in real-word aircraft engine sound test.
