Abstract
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.
Highlights
In daily life conditions, we often need to recognize many kinds of environmental sound
Aircraft engine sound signal contains a lot of important information, which makes the aircraft engine sound signal recognition being a very important tool for engine status diagnosis [1,2]
Harma.M proposed an audio monitoring system using in the office environment [3]
Summary
We often need to recognize many kinds of environmental sound. Harma.M proposed an audio monitoring system using in the office environment [3] They combine the tone and spectrum centroid features to detect the events occurred in the office. In [4], an audio classification system for detecting crime in the elevator is reported Their system uses GMM models to classify and identify alarm sounds events. In [7], Zhou proposed to construct feature set including MFCC and logarithmic domain subband energy They use Adboost algorithm to select effective features to detect conference room sound event. In [8, 9], the researchers developed an audio monitoring system that uses MFCC and short-term energy as sound event characteristics. GMMs were used as recognition models to detect abnormal sound events in the elevator environment. The system can effectively recognize abnormal engine sound only require few data
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